Optimized Digital Image Watermarking for
Uncorrelated Color Space
A Thesis
submitted in partial fulfillment of the requirements for the award of
the degree of
Doctor of Philosophy
by
Manish Gupta
Enrolment No.: 11E7UCPEM4XP900
Supervisor(s):
Dr. Rajeev Gupta
Dr. Girish Parmar
Department of Electronics Engineering
Rajasthan Technical University, Kota
Rajasthan, India
December, 2015
c
RAJASTHAN
TECHNICAL UNIVERSITY, KOTA, 2015
ALL RIGHTS RESERVED
Dedicated to
Lord Shri Goverdhan M aharaj Ji,
Lord Shri Banke Bihari Lal Ji,
My F ather Shri Kalika P rasad Gupta,
Mother Smt. Rajkumari Gupta,
Brother N eeraj,
Wif e Ruchi
and
Daughter Khyati & Son Anant
Acknowledgements
I would like to express here my sincere gratitude to my supervisors, Dr.
Rajeev Gupta, Professor, RTU, Kota and Dr. Girish Parmar,
Associate Professor, RTU, Kota, whose precious advice and friendly
encouragement made this work go smoothly throughout the period of
research. They are the source of never-ending inspiration for me. I
have been extremely lucky to have them as my mentors. Their precise
and lucid thoughts have helped me to manage many responsibilities
comfortably. They not only encouraged me to work but also gave
his guidance, experience, constructive thoughts and took keen interest
throughout the course of work and preparation of manuscript which
had made me worth that I claim to be now. Working with them made
me more independent and liable, which I believe is the most important
aspect in anyone’s career. I am indebted to them more than they
recognizes.
No appropriate words could be traced in the presently available lexicon
to avouch the excellent guidance given by Prof. R. S. Meena, Head,
Electronics Engineering Department at RTU, Kota, Prof. Mithilesh
Kumar, Principal, GEC, Jhalawar (Raj.), Prof. Ranjan Maheshwari,
Prof. Lokesh Tharani, Prof. Jankiballabh Sharma, Prof. Pankaj
Shukla, and Prof. Praveen Kumar. It is my great privilege to mention
the entire official staff for helping me in the official documentation
work right from the time of my registration into Ph.D. Moreover, I
would like to thanks to my research committee members and faculty
members of the institute for their help and support.
I am extremely thankful to Dr. Mukesh Sarsawat who has given his
constant guidance, support and encouragement throughout this study.
His expertise, experience, vigorous efforts and co-operation had made
me to complete this work. Without his unfailing support and belief in
me, this thesis would not have been possible.
I am extremely grateful to Hindustan Institute of Technology and
Management, Agra administration, Dr. A. K. Gupta, Dr. Shailendra Singh, Dr. N. P. Singh, Mr. Jayash Kumar Sharma, Mr. Santosh
viii
Kumar Dwivedi, Mr. Kamal Karakoti, Mr. Viresh Chauhan and Mr.
Sudhir Verma for their cooperation during my stay at RTU, Kota.
I express my gratitude to Prof. Rajiv Saxena and Prof. Dinesh Chandra for their constructive suggestions offered to me for successful completion of my thesis.
I am sincerely thankful to my friends Mr. Jayash Sharma, Mr. Mahendra Kumar Pandey, Mr. Abhay Chaturvedi, Mr. Rajesh Kumar
Bathija, Mr. Pradeep Singh Bhati, Mr. Narbada Prasad Gupta, Mr.
Puneet Kumar Choudhary, Mr. Vikas Rai, Mr. Vijay Kumar Dixit,
Mr. Deepak Bhatia and Mr. Neeraj Jain for their scholastic guidance,
suggestions and encouragement throughout the study duration. Along
with them, I am also thankful to Mrs. Rita Saini and other Ph.D.
scholars for providing their kind helps and supports during the time of
research.
I owe special thanks to all my friends who encouraged and boosted
my morale during the difficult days particularly Mr. S C Gupta, Mr.
Lokendra Sharma, Mr. Shyam Sunder Agrawal, Mr. Sanjay Mishra,
Mr. Manish Agrawal, Mr. Hitendra Garg, Mr. Raj Kumar Verma,
Mr. Manish Oberoi, Mr. Amit Jaiswal, Mr. Anurag Saxena, Mr.
Suneet Parashar, Mr. Darpan Anand, Mr. Rahul Saraswat, Mr. B
N Gupta, Mrs. Nisha Agrawal, Mrs. Hemlata Yadav, Ms. Priyanka
Yadav, Mrs. Priyanka Agrawal, Mr. Gaurav Sharma, Mr. R N Singh,
Mr. P S Parihar, Mr. Santosh Sahu, Dr. A K Verma, Mr. Ajitesh
Kumar, Mr. Brajesh Sharma, Mr. Rahul Sighal, Mr. Nitin Sharma,
and Mrs. Neema Verma.
I would like to offer my most humble gratitude to my Late grandfather,
grand mother, and maternal grandmother.
I would like to offer my most humble gratitude to my maternal grandfather Shri Ramsewak Neekhara, parents, my uncle and aunty, my
Mausaji and Mausiji, my Mamaji and Mamiji, my brother in-laws
Mr. Vishal Gupta and Mr. Saurabh Gupta, my brothers Neeraj,
Anand, Vinay, Naveen, Ram, Shayam, and Vaibhav, my sisters Suruchi, Sonam, Kritika, Himanshi, and Chritanshi, my nephew Vikhyat,
my in-laws Shri S. L. Gupta, Mrs. Neelam Gupta, Mr. Santosh Gupta,
Mr. Manoj Gupta, Mr. Sachin Gupta and my wife Ruchi, my daughter
Khyati, my son Anant, and other family members for their unsurpassed
love, care, endless patience and countless sacrifices which they made
ix
for me. Indeed, a plethora of words would not suffice to say what I
owe to them.
Last but not the least I am highly tankful to Lord Shri Goverdhan Maharaj Ji and Lord Shri Banke Bihari Lal Ji for showing his blessings
and brings this bright day in my career.
(Manish Gupta)
x
Abstract
Digital image watermarking is a process of imperceptibly embedding
watermark in the form of signature, random sequence or some image
into an image (host) which may be used to verify the genuineness of its
owner. Watermarking could be used for various real-time applications
like, protection of intellectual property rights (IPR) of multimedia contents, forensics and piracy deterrence, content filtering, document and
image security, broadcast monitoring, etc. Robustness of digital image
watermarking methods are of paramount importance in the perspective of the protection of multimedia contents due to easily accessible
data manipulation tools and the strengthening of high data rate transfer over the internet. Therefore, there is a growing interest in developing a method to protect multimedia contents. However, no single
watermarking method can be used to all applications. Therefore, the
primary objective of this work is to design and develop the robust digital watermarking methods for the protection of color images from its
unauthorise utilization.
The proposed watermarking methods consist of five steps. First, host
and watermark images are pre-processed using uncorrelated color space.
Second, the watermark is embedded into the host image using four different embedding methods. Third, post-processing is done by embedding the coefficients into the watermarked image. Fourth, performance
of proposed watermarking methods is improved using various optimization methods namely; genetic algorithm (GA), artificial bee colony
(ABC), and differential evolution (DE). Finally, the extracted watermark is verified for the genuineness of its owner. The performance of
developed methods are measured under various signal processing and
geometric attacks.
Generally, researchers used RGB, Y Cb Cr , YIQ, HSI, or HSV, etc. color
space models for both watermarks and host images in their digital
watermarking methods which are correlated in nature and impose the
restriction to use any one color component at a time for embedding
the watermark. However, there exists some uncorrelated color models
xii
such as Lab, Lαβ, etc., which may be used in image watermarking
to increase the robustness and quality by using all the color image
components of host and watermark images. Therefore, in this thesis,
a recently developed uncorrelated color space (UCS) has been utilized
during the pre-processing step. Experimental results show that the
UCS method outperforms other existing methods.
Embedding the watermark image into host image is the second step
of proposed watermarking method. The selection of embedding domain is the most vital and difficult task of any watermarking methods.
In this thesis, two transform methods have been used for embedding
the watermark into the host image namely; discrete wavelet transform
(DWT) and steerable pyramid transform (SPT). Both the methods
transform the host image and utilize their transform coefficients for
hiding the pre-process watermark data. After hiding the watermark
data into transform coefficients, post-processing is done to generate
watermarked image. The experimental results show that steerable
pyramid transform (SPT)-based method is better than other existing
methods of digital watermarking.
There is one component during the embedding process namely strength
factor which effects the performance of watermarking methods. Therefore, there is a requirement to use its optimum value. For the same, the
proposed watermarking methods exploit optimization methods (GA,
ABC, and DE) to calculate the optimum strength factors. Among
other optimization methods, DE improves the performance as compared to other existing methods.
Finally, the performance of the proposed methods are tested after applying various signal processing and geometric attacks. In this thesis,
20 different attacks have been used for validation of the proposed methods.
The output of this research work is a robust digital image watermarking
method for the protection of color images against the various malicious
attacks.
Keywords: Image watermarking, Discrete wavelet transform, Steerable pyramid transform, Uncorrelated color model, Genetic algorithm,
Artificial bee colony, Differential evolution.
xiii
Table of Contents
Title
Page No.
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
. viii
List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xx
Chapter 1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.1 Motivation for Research . . . . . . . . . . . . . . . . . . . . . . . .
1
1
1.2
1.3
Applications of Watermarking . . . . . . . . . . . . . . . . . . . . .
Requirements and Design Issues of Digital Watermarking Method .
1.4
1.5
1.6
Taxonomy of Watermarking Attacks . . . . . . . . . . . . . . . . . 7
Classification of Watermarking Methods . . . . . . . . . . . . . . . 10
Literature Review of Image Watermarking . . . . . . . . . . . . . . 12
1.6.1
1.6.2
1.7
1.8
2
4
Spatial Domain-based Methods . . . . . . . . . . . . . . . . 13
Transform Domain-based Methods . . . . . . . . . . . . . . 14
1.6.3 Optimization Methods in Image Watermarking . . . . . . . 20
1.6.4 Color Spaces in Image Watermarking Method . . . . . . . . 22
Challenges in Image Watermarking . . . . . . . . . . . . . . . . . . 24
1.7.1
1.7.2
Use of Color Watermark . . . . . . . . . . . . . . . . . . . . 24
Color Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.7.3
1.7.4
1.7.5
Transform Method . . . . . . . . . . . . . . . . . . . . . . . 25
Optimization Method . . . . . . . . . . . . . . . . . . . . . . 25
3-D Watermarking . . . . . . . . . . . . . . . . . . . . . . . 25
Scope of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Chapter 2 Digital Image Watermarking using Discrete Wavelet
Transform on Gray-Scale Watermark Image . . . . . . . 29
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.2
Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
xiv
2.3
2.4
2.5
2.2.1
Discrete Wavelet Transform (DWT) . . . . . . . . . . . . . . 31
2.2.2
2.2.3
Uncorrelated Color Space (UCS) . . . . . . . . . . . . . . . 33
Genetic Algorithm (GA) . . . . . . . . . . . . . . . . . . . . 33
Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . 44
Chapter 3 Digital Image Watermarking using Steerable Pyramid
3.1
3.2
Transform on Gray-Scale Watermark Images . . . . . . 45
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Steerable Pyramid Transform (SPT) . . . . . . . . . . . . . . . . . 46
3.3
3.4
Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.5
Results and Discussions
. . . . . . . . . . . . . . . . . . . . . . . . 51
Chapter 4 Digital Image Watermarking using Discrete Wavelet
Transform on Color Watermark Images . . . . . . . . . . 57
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
4.2
4.3
Artificial Bee Colony (ABC) Method . . . . . . . . . . . . . . . . . 59
Differential Evolution (DE) Algorithm . . . . . . . . . . . . . . . . 61
4.4
4.5
4.6
Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Method Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 69
4.7
Results and Discussions
. . . . . . . . . . . . . . . . . . . . . . . . 73
Chapter 5 Digital Image Watermarking using Steerable Pyramid
Transform on Color Watermark Images . . . . . . . . . . 81
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
5.2
5.3
Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Method Validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5.4
5.5
Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 87
Results and Discussions . . . . . . . . . . . . . . . . . . . . . . . . 97
Chapter 6 Conclusions and Scope for Future Work . . . . . . . . . 99
6.1 Contributions Made in the Thesis . . . . . . . . . . . . . . . . . . . 100
6.2
Scope for Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 101
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
List of Publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
xv
List of Figures
Figure No.
Title
Page No.
1.1
1.2
Mutual dependency between the design parameters. . . . . . . . . .
Three main conflicting issues of watermarking. . . . . . . . . . . . .
5
5
1.3
1.4
Classification of watermarking methods. . . . . . . . . . . . . . . . 10
General block diagram of transform domain-based image watermarking system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.1
2.2
Three level decomposition layout of an image. . . . . . . . . . . . . 32
Genetic Algorithm Flow Chart. . . . . . . . . . . . . . . . . . . . . 34
2.3
2.4
2.5
Structural design of proposed method. . . . . . . . . . . . . . . . . 36
(a)-(b). RGB host images and (c)-(d). Gray scale watermark images. 39
Extracted RTU logo watermarks by proposed method after applying
2.6
considered attacks. . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
Extracted Aeroplane image watermarks by proposed method after
applying considered attacks. . . . . . . . . . . . . . . . . . . . . . . 43
3.1
3.2
Block diagram for steerable pyramid decomposition of an image. . . 47
Structural design of proposed method. . . . . . . . . . . . . . . . . 48
3.3
3.4
(a)-(b). RGB host images and (c)-(d). Gray scale watermark images. 50
Extracted RTU logo watermarks by proposed method after applying
3.5
considered attacks. . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Extracted Aeroplane image watermarks by proposed method after
applying considered attacks. . . . . . . . . . . . . . . . . . . . . . . 54
4.1
4.2
4.3
Structural design of proposed method. . . . . . . . . . . . . . . . . 65
Watermark division. . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Partitioning of 3-DWT coefficients. . . . . . . . . . . . . . . . . . . 66
4.4
4.5
RGB host images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
RGB watermark images. . . . . . . . . . . . . . . . . . . . . . . . . 70
4.7
4.6
Three level decomposed host lena image using DWT. . . . . . . . . 70
UCS host and watermark images (a). Lena, (b). Mandrill, (c).
Pepper, (d). Sailboat, (e). RTU Logo, and (f). Aeroplane. . . . . . 71
xvi
4.8
Representative scrambled UCS watermark images. . . . . . . . . . . 71
4.9
RGB watermarked images embedded by using DE-based proposed
method (a)-(d) RTU logo and (e)-(h) Aeroplane image . . . . . . . 72
4.10 Comparison of extracted watermarks by considered and proposed
methods along with their corresponding NC values. Columns shows
extracted watermarks from watermarked image namely (a). Lena,
(b). Mandrill, (c). Pepper, and (d). Sailboat using the considered
and proposed methods mentioned in first column. First five rows
shows the extraction of RTU logo while last five shows extraction
of aeroplane watermark image. . . . . . . . . . . . . . . . . . . . . 74
4.11 Extracted RTU logo watermarks by proposed method using DE
after applying considered attacks. . . . . . . . . . . . . . . . . . . . 78
4.12 Extracted Aeroplane watermarks by proposed method using DE
after applying considered attacks. . . . . . . . . . . . . . . . . . . . 79
5.1
5.2
Structural design of proposed method. . . . . . . . . . . . . . . . . 83
Watermark division. . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.3
5.4
5.5
RGB host images. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
RGB watermark images. . . . . . . . . . . . . . . . . . . . . . . . . 87
UCS host and watermark images (a). Lena, (b). Mandrill, (c).
5.6
Pepper, (d). Sailboat, (e). RTU Logo, and (f). Aeroplane. . . . . . 88
Representative scrambled UCS watermark images. . . . . . . . . . . 88
5.7
5.8
RGB watermarked images embedded by (a)-(d) RTU logo and (e)(h) Aeroplane image . . . . . . . . . . . . . . . . . . . . . . . . . . 89
A comparison of before and after optimization of fitness values for
5.9
30 runs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Comparison of extracted watermarks by considered and proposed
methods along with their corresponding NC values. Columns shows
extracted watermarks from watermarked image namely (a). Lena,
(b). Mandrill, (c). Pepper, and (d). Sailboat using the considered
and proposed methods mentioned in first column. First four rows
shows the extraction of RTU logo while last four shows extraction
of aeroplane watermark image. . . . . . . . . . . . . . . . . . . . . 91
5.10 Extracted RTU logo watermarks by proposed method using UCS
and DE after applying considered attacks. . . . . . . . . . . . . . . 95
5.11 Extracted Aeroplane watermarks by proposed method using UCS
and DE after applying considered attacks. . . . . . . . . . . . . . . 96
xvii
List of Tables
Table No.
Title
Page No.
1.1
Applications of watermarking methods. . . . . . . . . . . . . . . . .
3
1.2
1.3
Requirements and design issues of watermarking methods. . . . . .
Taxonomy of watermarking attacks. . . . . . . . . . . . . . . . . . .
5
8
1.4
1.5
1.6
Categories of spatial domain-based image watermarking method. . . 14
Categories of transform domain-based image watermarking method. 17
Categories of optimization methods in image watermarking. . . . . 21
1.7
Categories of color spaces in image watermarking method. . . . . . 23
2.1
Comparison of CPSNR values of watermarked images resultant
2.2
from proposed and considered method. . . . . . . . . . . . . . . . . 40
Comparison of robustness in terms of NC values obtained after applying attacks on the watermarked images. . . . . . . . . . . . . . . 41
3.1
Comparison of CPSNR values of watermarked images resultant
from proposed and considered methods. . . . . . . . . . . . . . . . . 51
3.2
Comparison of robustness in terms of NC values obtained after applying attacks on the watermarked images. . . . . . . . . . . . . . . 52
4.1
Setting of the parameters of three optimization methods namely;
4.2
GA, ABC, and DE. . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
Comparison of CPSNR and SSIM values of watermarked images
4.3
4.4
4.5
resultant from proposed and considered methods. . . . . . . . . . . 72
Average subjective quality comparison of original and watermarked
images by 10 human beings in the scale of 0 to 5. . . . . . . . . . . 72
Comparison of robustness in terms of NC values obtained after applying common signal processing attacks on the watermarked image
embedded with RTU logo . . . . . . . . . . . . . . . . . . . . . . . 75
Comparison of robustness in terms of NC values obtained after applying common signal processing attacks on the watermarked image
embedded with Aeroplane image. . . . . . . . . . . . . . . . . . . . 76
xviii
4.6
Comparison of robustness in terms of NC values obtained after applying geometric attacks on the watermarked images. . . . . . . . . 77
5.1
Comparison of CPSNR and SSIM values of watermarked images
5.2
resultant from proposed and considered methods. . . . . . . . . . . 90
Average subjective quality comparison of original and watermarked
images by 10 human beings in the scale of 0 to 5. . . . . . . . . . . 90
5.3
5.4
5.5
Comparison of robustness in terms of NC values obtained after applying common signal processing attacks on the watermarked image
embedded with RTU logo . . . . . . . . . . . . . . . . . . . . . . . 92
Comparison of robustness in terms of NC values obtained after applying common signal processing attacks on the watermarked image
embedded with Aeroplane image. . . . . . . . . . . . . . . . . . . . 93
Comparison of robustness in terms of NC values obtained after applying geometric attacks on the watermarked images. . . . . . . . . 94
xix
List of Abbreviations
ABC
ACO
Artificial Bee Colony
Ant Colony Optimization
CPSNR
DCT
DE
Composite Peak Signal-to-Noise Ratio
Discrete Cosine Transform
Differential Evolution
DFT
DWT
Discrete Fourier Transform
Discrete Wavelet Transform
EA
GA
HSI
Evolutionary Algorithm
Genetic Algorithms
Hue Saturation Intensity
HSV
HVS
ICA
Hue Saturation Value
Human Visual System
Independent Component Analysis
IDCT
IDFT
Inverse Discrete Cosine Transform
Inverse Discrete Fourier Transform
IDWT
IPR
ISPT
Inverse Discrete Wavelet Transform
Intellectual Property Rights
Inverse Steerable Pyramid Transform
JPEG
LSB
Joint Photographic Expert Group
Least Significant Bit
MPEG
NC
PCA
Moving Picture Expert Group
Normalized Correlation
Principal Component Analysis
PSO
QS
Particle Swarm Optimization
Quantization Step
RGB
SA
SPIHT
Red Green Blue
Simulated Annealing
Set Partitioning In Hierarchical Trees
SPT
SSIM
Steerable Pyramid Transform
Structural Similarity
xx
SV
Singular Value
SVD
UCS
Singular Value Decomposition
Uncorrelated Color Space
Y Cb Cr
YIQ
YUV
Luminance Chroma: Blue Chroma: Red
Luminance In-phase Quadrature
Luminance Blueluminance Redluminance
xxi
Chapter 1
Introduction
The digital image watermarking method is capable to resolve the issue of ownership
and hence, this area of research has a wider set of applications such as copyright
protection, content authentication, ownership identification, etc. Bender et al. [1],
Fotopoulos et al. [2], Potdar et al. [3], and Liu et al. [4] presented a state-of-theart survey of image watermarking methods which have been applied in various
applications. This thesis is an attempt to develop a digital image watermarking
method for copyright protection. This chapter presents the motivation of the
research, application areas, requirement and design issues, taxonomy of attacks,
and classification of watermarking methods. Further, some of the challenges in the
field of image watermarking are identified. Moreover, a detailed survey of relevant
research in digital image watermarking is presented.
1.1
Motivation for Research
In current digital era, the orderly growth of easily capturable multimedia data
devices (such as digital cameras, camcorders, and scanners), efficient compression algorithms for multimedia data, and digital data transmission speed over the
internet have enable widespread applications, which rely on digital data. Digital
multimedia data offers many advantages over its analog counterpart like high quality, easy editing, easily stored and copying without loss of fidelity, etc. Though,
the digital data possesses many inherent advantages over its analog version, the
genuineness or ownership is the biggest challenge. The digital multimedia data can
be easily duplicated and/ or manipulated which results the real threat for content
owner from the misuse of their work or data. To keep on with the transmission
of multimedia data over the high speed internet the reliability and originality of
the transmitted multimedia data should be verifiable. In view of these deleterious
findings, it is necessary and need of current digital era that the multimedia data
must be protected and secured against the illegal utilization.
Today, multimedia content owners are eagerly seeking technologies that promise
to protect their rights and secure their content from piracy, unauthorized usage,
and enable the tracking and conviction of media pirates. During the past decades,
various solutions were proposed by the researchers in order to protect the multimedia data against the unauthorized utilization. One of the solutions of this problem
is to embed an invisible data into the original multimedia data to proof the ownership of the data. This type of method is named as information hiding which can
be further classified into various sub-classes such as cryptography, steganography,
and watermarking [5, 6]. Cryptography is the most common method of protecting
digital multimedia data, where the multimedia content is encrypted preceding to
release and a decryption key is provided to those who have purchased genuine or
legal copies [7, 8, 9]. However, cryptography cannot assist the content providers
monitor their contents after the decryption process; a buccaneer could easily purchase a genuine or legal copy and then resell it or distribute it for free over a
public network. Further, steganography is about preventing the detection of an
encrypted data, which has been protected by cryptography algorithms. However,
the message hide by steganography is not robust. Watermarking has an extra
requirement of robustness compared to steganography algorithms against various
signal processing and geometric attacks. It is therefore vital to find a path to protect these digital multimedia contents with a more precise method, which would
enable the content owners to get confidence in placing and distributing their material over the internet. Watermarking could be such a vehicle. Therefore, this thesis
plans to design and develop an efficient and robust digital image watermarking
method for protecting the color images against various attacks.
Following section illustrates the various applications of watermarking methods.
1.2
Applications of Watermarking
Digital watermarking methods have many applications namely; copyright protection, content authentication, fingerprinting, broadcasting monitoring, secret
communication, medical safety, etc. [10, 11, 12, 13] as discussed in the following
sections and summarized in Table 1.1.
• Copyright Protection: One of the motivations of developing the watermarking methods is copyright protection. In this application, a copyright
data/ information is embedded into host object without loss of quality [14].
The embedded data prevents other parties from claiming the ownership of
2
Table 1.1: Applications of watermarking methods.
S.No.
Application
Description
1.
Copyright Protection
The watermark must be known only to the author and also most
importantly it must be robust against the various attacks.
2.
Covert Communication
Since various offices or governments put a ceiling on the use
of encryption. In this scenario people may send their secret
messages by using the watermarking method.
3.
Copy Control
This application restrict the illegally copying of copyrighted materials by embedding a never-copy watermark or limiting the
number of times of copying.
4.
Content Authentication
Fragile watermark could be embedded into the host image to
check the authenticity of the data.
5.
Fingerprinting
Fingerprinting method used by the owner is to trace the source
of illegal copies.
6.
Broadcast Monitoring
Owners of copyrighted programs needs to know about illegal
broadcast, aired by the broadcasters, at the time and location
that they want according to the contracts terms and conditions.
7.
Medical Safety
Embedding the date and the patients name in medical images
could be a useful safety measure.
8.
Indexing
Indexing of multimedia contents like movies, news items, video
mail, images, etc., to help search engines to search those contents
over the internet.
that data. Moreover, the watermark must be known only to the author and
must be robust against the various attacks.
• Covert Communication: Watermarking methods can also be used for
the covert information transmission, as various offices or governments put
a ceiling on the use of encryption. In this scenario people may send their
secret messages by using the watermarking methods.
• Copy Control: This application restricts the illegal copying of copyrighted
materials by embedding a never-copy watermark or limiting the number of
times of copying. For example, today many documents are available on the
internet which could not be saved and printed to control the illegal copying.
• Content Authentication: Fragile watermark could be embedded into the
host image to check the authenticity of the data. A fragile watermark indicates whether the data has been altered and also delivers the information as
to where the data was altered. Therefore, this application does not demand
the robust watermark, since we have to detect the changes only.
• Fingerprinting: Fingerprinting method, used by the owner, is to trace the
source of illegal copies. To achieve this, owner can embed different watermarks into each copy that distributed to a different customer. For example,
3
unique serial numbers are assigned to customers and used to identify the
customer.
• Broadcast Monitoring: Owners of copyrighted programs needs to know
about illegal broadcast or the commercials, aired by the broadcasters, at
the time and location that they want according to the contracts terms and
conditions. Watermarks can be embed in any type of data to broadcast on
the network by automated systems, which are able to monitor distribution
channels to track the content in the time and the place that they appear.
• Medical Safety: Recently, telemedicine facilitates medical diagnosis by
sending patient medical data/ report over the public network for further
analysis where the modern medical equipments are available. These equipments produce large amount of data every day. Hence, it is necessary to
protect these crucial data. Medical image watermarking is a suitable method
used for enhancing security and authentication of medical data, which is used
for further diagnosis and reference. Embedding the date and the patients
name in medical images could be a useful safety measure.
• Indexing: One of the well-known application of the watermarking is indexing of multimedia contents like movies, news items, video mail, images, etc.
In which a comments or any tag/ level is embedded on the contents, so that
these comments or tags are utilized by any search engine to search those
contents over the internet.
The following section listed various requirements and design parameters related
to watermarking methods.
1.3
Requirements and Design Issues of Digital
Watermarking Method
There are various design issues and requirements associated with any watermarking method like transparency, robustness, capacity, security, etc. as summarized
in Table 1.2. The objectives of researchers in the field of watermarking is to maximize all these parameters for a particular method. Furthermore, these parameters
are mutually dependant on each other as shown in Figure 1.1. Three parameters
namely; transparency, robustness, and capacity are inversely related to each other
i.e. the transparency of a watermarking method increases then its robustness
suffers and vice-versa. This relationship has been depicted in Figure 1.2.
4
Table 1.2: Requirements and design issues of watermarking methods.
S.No.
Requirements and Design
Issues
Description
1.
Transparency
No visual or audio effect should be noticed by the user.
2.
Robustness
Watermark can be robust against one operation on host data
and may be fragile against another operation.
3.
Capacity
If the capacity is higher than the better robustness is achieved
and at the same time transparency suffers or vice-versa.
4.
Security
The watermark must resist against the attacks on the host
data.
5.
Complexity
The computational cost must be as low as possible to make
the applications real time.
6.
Reliability
Watermark data embedded into the host, must be recoverable
with the acceptable errors.
Figure 1.1: Mutual dependency between the design parameters.
Figure 1.2: Three main conflicting issues of watermarking.
Hence, the relative importance of these parameters depends on the applicationto-application as listed in the previous section. Moreover, certain applications
demand for more robustness compared to the transparency of the method viz.
copyright protection. Therefore, watermarking method design process involves
trade-off between the conflicting requirements parameters. The most important
requirement for digital watermarking are summarized below:
• Transparency: Transparency or imperceptibility refers to the correlation/
similarity between the watermarked data and the original data. The wa5
termark should be invisible. In other words, there is no visual or audio
effect should be noticed by the user. The watermark should not be disgrace
the quality of the host data. However, for a particular application minute
degradation in the host data is permissible to achieve better robustness or
to optimize the cost.
• Robustness: If a watermark can stay alive after common signal processing
operations (such as compression, filtering, translation, rotation operations,
analog-to-digital conversion, scaling, etc.) on host data, then such type of
watermark is called the robust. Moreover, watermark can be robust against
one operation on host data and may be fragile against another operation. For
certain applications, there is a need to embed a robust watermark into the
host data, while some applications demand the fragile watermark. Hence,
it also depends on the application. If the watermark data is embedded in
significant area of a host image then the better robustness is achieved. This
is because those area do not alter so much after common image processing
operations [15]. Contrary to robust watermark, a fragile watermark is not
designed to be robust.
• Capacity: A capacity or payload refers the amount of watermark data that
can be embedded into host. For example, the capacity in case of image
watermarking means the number of bits embedded within the host image.
If the payload is higher than the better robustness is achieved and at the
same time transparency suffers or vice-versa. Therefore, the payload of the
embedded watermark must be in sufficient amount to enable the envisioned
application.
• Security: The watermark must resist against the attacks on the host data.
It must be impossible for an attacker to delete or modify the watermark
without rendering the multimedia data unusable. From this point of view, a
secret watermark key is also used in watermarking, so that it is not possible
to retrieve or even modify the watermark without knowledge of the key.
• Complexity: Depending on the application, the watermark detection is to
be done at different speeds and complexity. For example in broadcast monitoring application, the detection of the watermarking is done in real time.
The computational cost must be as low as possible to make the applications
real time. To keep above facts, the complexity of the watermarking methods
should be low.
6
• Reliability: Watermark data, embedded into the host, must be recoverable
with the acceptable errors.
The performance of watermarking methods for achieving above mentioned requirements are tested after applying various attacks. Therefore, the following
section describes the classification of attacks applicable to image watermarking.
1.4
Taxonomy of Watermarking Attacks
Any procedure that can decrease the performance of watermarking method may be
termed as attack. Testing the robustness and security of a watermarking method
against attacks is as important as the design process. The attacks do not always
remove or destroy the watermark but, also disable its detection. The distortions
done by any attacks degrade the performance of the watermarking method.
In general, different attacks on watermarking can be divided into two classes
namely; unintentional and intentional attacks. To achieve the high reliability
of watermark detection, the watermark detection process has to be robust to the
modifications in the host data caused from both unintentional and intentional
attacks. Unintentional attacks take place using signal processing operations on
watermarked data namely; compression, printing, scanning, filtering, noise, geometric transforms, cropping, etc. For example, multimedia data is generally stored
in lossy compressed format in order to use less storage capacities. These compression algorithms discard the unimportant parts of data. This distortion may cause
damage of inserted watermark data too. This means that a simple attack is compressing multimedia data in a lossy way. In addition, a rotation or scaling can
change pixel values and destroy the watermark data. Signal processing operations
such as quantization, decompression, re-sampling, and color reduction can damage the watermark. For intentional attacks, a person on purpose can attack on
inserted watermark data in order to copy the multimedia data. In both cases,
any watermarking method should be able to detect and extract the watermark
after attacks. The taxonomy of various intentional and unintentional attacks in
watermarking methods [16, 17, 18], are presented in Table 1.3 and their details
are summarized below:
• Noise: Any random unwanted signal with a given distribution namely; Gaussian, salt & pepper, Poisson, etc., is added to the image unintentionally.
This type of noise may be added during the Analog-to-Digital conversion
and vice-versa, or as a result of transmission errors. However, an attacker
may introduce perceptually shaped noise with the maximum un-noticeable
7
Table 1.3: Taxonomy of watermarking attacks.
S.No.
Attack
Details
1.
Noise
Any random unwanted signal with a given distribution namely; Gaussian, salt & pepper, Poisson, etc., is added to the image unintentionally.
2.
Filtering
Filtering attacks are linear filtering namely; low pass/ mean filtering,
Gaussian, and sharpening filtering, etc.
3.
Compression
If the watermark is required to resist different levels of compression, it
is usually advisable to perform the watermark embedding in the same
domain where the compression takes places.
4.
Multiple Watermarking
The one of the solution of such type of problem is to embedding the
time information by a certification authority.
5.
Geometrical Attacks
Geometrical attacks distort the watermark through spatial alterations
of the watermarked image. Common geometrical attacks are rotation,
scaling, etc.
6.
Cropping
This is a very common attack which crops the region of interest from
the watermarked object.
7.
Watermark
Removal
and Interference Attacks
The objective of such attacks is to forecast or estimate the watermark.
8.
Statistical Averaging
The objective of such attacks is to recover the host image and/or
watermark data by statistical investigation of multiple marked data
sets.
power. This will characteristically force to increase the threshold at which
the correlation detector operates.
• Filtering: Filtering attacks are linear filtering namely; low pass/ mean filtering, Gaussian, and sharpening filtering, etc. Mean or average filtering does
not introduce considerable degradation in watermarked images but, can dramatically affect the performance. Therefore, to design a watermark, robust
to a known group of filters that might be applied to the watermarked image,
the watermark data should be designed in such a way that it have most of
its energy in the frequencies which filter transfer functions changes the least.
• Compression: Compression belongs to an unintentional attack class, which
appears very often in various multimedia applications. In practice, most of
the images, audio, and video are being transmitted/ distributed via internet
after the compression in order to reduce the time and data usage. If the
watermark is required to resist different levels of compression, it is usually
advisable to perform the watermark embedding in the same domain where
the compression takes places.
• Multiple Watermarking: An attacker may watermark an already watermarked data and later claims of ownership. One solution to such type of
8
problem is to embed the time information by a certification authority.
• Geometrical Attacks: Geometrical attacks do not pretend to remove the watermark by itself, but to distort it through spatial alterations of the watermarked image. With such attacks watermarking detector loses the synchronization with the embedded information. These attacks can be subdivided
into attacks applying general affine transformations and attacks based on
projective transformation. Common geometrical attacks are rotation, scaling, change of aspect ratio, translation and shearing, etc.
• Cropping: This is a very common attack since in many cases the attacker is
interested in a small portion of the watermarked object, such as parts of a
certain picture or frames of video sequence. With this in mind, in order to
survive, the watermark needs to be spread over the dimensions where this
attack takes place.
• Watermark Removal and Interference Attacks: The objective of such attacks
is to forecast or estimate the watermark and then use the predicted watermark either to eradicate watermark or to damage its unique extraction at the
destination side. Some known efficient removal attacks are; the median watermark prediction followed by subtraction [19], the Wiener prediction and
subtraction [18] and perceptual re-modulation [20], which combines both
removal and interference attacks.
• Statistical Averaging: The objective of such attacks is to recover the host
image and/or watermark data by statistical investigation of multiple marked
data sets. An attacker may attempt to predict the watermark and then to
remove the watermark by subtracting the estimate. This is very hazardous
if the watermark does not rely significantly on data. That is why the perceptual masks are used to create a watermark. Averaging or smoothing attack
is belonging to this class of attack. Averaging attack consists of averaging
many instances of a given data set (e.g. N) each time marked with a different
watermark. In this pattern a prediction of host data is calculated and each
of the watermarks is weakened by a factor N.
In the above section, various classification of watermarking attacks have been
presented. The following section gives the brief details of classification of the
different watermarking methods.
9
1.5
Classification of Watermarking Methods
Mohanty [21] presented a state-of-the-art categorization of digital watermarking
methods. Digital watermarking methods may be categorized on the basis of host
multimedia data, human perception, embedding domain, robustness, data extraction, application area, etc. [13]. Figure 1.3 shows the classifications of digital
watermarking methods. There are different way of classification of digital water-
Figure 1.3: Classification of watermarking methods.
mark methods [22].
• First, watermarking methods may be divided into four groups according to
the type of host multimedia data to be watermarked;
– Image watermarking method
– Audio watermarking method
– Video watermarking method
– 3-dimension (3-D) watermarking method
• Second, watermarking methods may be grouped on the basis of the data for
extraction;
– Private or non-blind watermarking method: This class of method required the original data (watermark or/ and host) during the extraction
of watermark from the watermarked data.
10
– Semi-private or semi-blind watermarking method : This group of method
needs extra information other than the original data during the detection.
– Public or blind watermarking method : In public or blind watermarking
method the detection of watermark from the watermarked data needs
only the watermark.
• Third, watermarking methods may be categorized on the basis of human
perception;
– Visible watermarking method : If the watermark data is noticeable to
the user, then such class of watermarking is known as visible watermarking method. Examples of visible watermarks are logos that are
used in papers and video.
– In-visible watermarking method : If the watermark data is imperceptible
to the user, then such class of watermarking is known as invisible watermarking method. For example, images distribute over the internet
and watermarked invisible for copy protection.
• Fourth, watermarking methods may also be classified on the basis of robustness;
– Fragile: A fragile watermark will be changed if the host data is modified.
– Semi-fragile: Semi-fragile watermark is sensitive to some degree of the
change to a watermarked image.
– Robust: Watermark in robust method cannot be removed by common
signal processing operations.
• Fifth, watermarking methods may also be classified on the basis of embedding watermark data;
– Text: If the embed watermark data is in the nature of text.
– Image format: If the embedded watermark belongs to any image shape
like logo, binary image, gray scale image, color image or logo, stamp,
etc.
– Noise sequence: In this class of method the embedding watermark is
in terms of random noise sequence like pseudo random or Gaussian
random sequence etc.
11
• Sixth, watermarking methods may be categorized on the basis of application
of method;
– Source-based watermarking method : In source based, all copies of a
particular data have a unique watermark, which identifies the owner of
that data.
– Destination-based watermarking method : In this method, each distributed copy is embedded using a unique watermark data, which identifies a particular destination.
• Finally, watermarking methods may be classified into two major classes according to the embedding domain;
– Spatial domain
– Transform domain;
∗ Discrete Fourier Transform (DFT) domain-based watermarking method
∗ Discrete Cosine Transform (DCT) domain-based watermarking method
∗ Discrete Wavelet Transform (DWT) domain-based watermarking
method
∗ Steerable Pyramid Transform (SPT) domain-based watermarking
method
∗ etc.
Following section reviews various image watermarking methods along with their
constraints.
1.6
Literature Review of Image Watermarking
In the previous Sections 1.2 – 1.5 the various application areas, requirements and
key design issues, classification and need of different watermarking attacks, and
classification related to digital watermarking methods have been discussed. In
the area of watermarking, image watermarking particularly attracted the research
community a lot because of following reasons;
• Easily availability of the test image databases.
• It contains adequate superfluous information to give an opportunity for embedding the watermark data easily.
• Any successful image watermarking method may be upgraded for the video
also.
12
• Wider set of applications.
Therefore, most of the research work in the field of watermarking is dedicated to
image as compared to audio, video, and other multimedia formats [23]. Therefore,
in this thesis the emphasis has been given only on the literature review of image
watermarking methods.
Digital image may be represented/ stored either in spatial/ time domain or
in transform domain. The spatial/ time domain image is characterize by pixels, whereas the transform domain image is described in terms of its transform
coefficients. In other words, transform domain representation of an image segregates the transform coefficients into multiple frequency bands. To convert an
image to its transform domain representation, we can use various available reversible transform methods namely; Discrete Fourier Transform (DFT), Discrete
Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Steerable Pyramid Transform (SPT), etc. Each of these transform method has its own specific
characteristics and representation of an image.
Digital image watermarking is a process of imperceptibly hiding a watermark
(in the form of signature, random sequence, or some image) into an image (host
or cover) which may be used to verify the genuineness of its owner. The resultant
image of this process is termed as watermarked image. The watermarking methods can be performed either in spatial domain or in the transform domain. In
spatial domain-based watermarking method, watermark may be embedded within
an image by modifying the pixel values [9] or the Least Significant Bit (LSB) values. While, in transform domain-based watermarking method, watermark may be
embedded by modifying the transform domain coefficients. However, more robust
watermark could be embedded in the transform domain of images by modifying
the transform domain coefficients as compared with the spatial domain-based image watermarking method. In the following section, the state-of-the-art review of
digital image watermarking methods have been presented based on spatial domain
and transform domain [1, 2, 3, 22, 24, 25, 26].
1.6.1
Spatial Domain-based Methods
A watermarking method based on the spatial domain approach, hides watermark
data in the pixel values of the host image. Such class of methods make minor
changes in the intensity of pixel value of host image [1, 27, 28, 29, 30, 31, 32, 33, 34].
One of the most common examples of this method is to embed the watermark in
the LSB’s of image pixels [28, 30, 35]. In other words, significant portions of
low frequency components of images should be modified in order to insert the
13
watermark data in a reliable and robust way. As another example, an image is
divided into the same size of blocks and a certain watermark data is added with
the sub-blocks [28]. The imperceptibility of the watermark data is achieved on the
postulation that the LSB bits are visually insignificant. Although, spatial domainbased watermarking method can be easily implemented and very fast, they have
the many disadvantages. These methods are highly susceptible to common signal
processing operations and can be easily impaired and tempered. For example,
lossy compression could completely crush the watermark data. In summary, watermarking method based on spatial is very easy to destroy using some attacks like
low-pass filtering, additive noise, etc. In other words, the spatial domain-based
image watermarking methods are not robust against the common signal processing operation on the host image. The brief summary of spatial domain-based
methods presented in Table 1.4. Researchers [15] suggested that the transform
domain-based image watermarking methods are more robust as compared to spatial domain-based image watermarking methods against the various watermarking
attacks as mentioned in the Section 1.4. Therefore, further in this thesis our focus
is only on the transform domain-based image watermarking methods.
Table 1.4: Categories of spatial domain-based image watermarking method.
S.No.
Category
Property
Description
1.
Pixel-based
[27, 28, 33]
Such class of methods makes minor changes in the intensity of
pixel value of host image to embeds a watermark. One of the
most common example of this
method is to embed the watermark in the LSB’s of image pixels.
Spatial domain-based watermarking method can be easily
implemented and very fast.
2.
Block-based
[28, 29, 30]
In this class of watermarking
method an image is divided into
the same size of blocks and a
certain watermark data is added
with the sub-blocks.
These methods are highly susceptible to common signal processing operations and can
be easily impaired and tempered. For example, compression could completely crush the
watermark data.
1.6.2
Transform Domain-based Methods
The transform of an image is just another form of representation. It does not
change the content present in the image. Transform domain-based image watermarking methods have many advantages over spatial domain-based methods
[15]. As presented in literature, transformed domain-based image watermarking
methods are more robust against the various watermarking attacks and signal
processing operations because the transform domain does not utilize the original
14
host image for casting the watermark data. In addition, the transform domainbased image watermarking distributes the watermark data over all part of the
host image. Moreover, transform domain-based methods are capable enough to
embed more watermark bits into the host image and are more robust to attack.
However, they are difficult to implement and are computationally more expensive
as compared with the methods given in Section 1.6.1. In literature, various reversible transform methods namely; DFT, DCT, DWT, SPT, etc. are used by the
researcher to improve the robustness of the image watermarking methods. This
class of watermarking methods insert the watermark data into the host image by
manipulating the corresponding transform coefficients [36, 37, 38, 39, 40, 41, 42,
43, 44, 45, 46, 47, 48].
Figure 1.4 shows the general block diagram of transform domain-based digital watermarking system. Summary of the transform domain-based methods are given
in Tables 1.5. A detailed sate-of-the-art survey of transform domain-based image
watermarking methods has been presented by Potdar et al. [3].
Discrete Fourier Transform (DFT)-based Method
Ruanaidh et al. [49] presented a DFT-based image watermarking method in which
the watermark data is inserted into the host image by manipulating the phase
information. Wolfgang et al. [30] later concluded in their work that image watermarking using the phase manipulation is robust against image contrast operation.
Further, Ruanaidh and Pun [50] presented a method of image watermarking using
the Fourier transform and concluded that method is robust against the geometric
attacks. Lin at al. [51] presented a novel image watermarking method which is robust against the rotation, scaling, and translation attacks. However, this method
is not robust enough against the cropping and compression attacks. In literature, few methods are available where the watermark is casted by modifying the
mid frequency band of DFT magnitude component [52, 53]. They concluded that
the proposed methods are robust against the Joint Photographic Expert Group
(JPEG) and Set Partitioning In Hierarchical Trees (SPIHT) compression attacks.
Moreover, Solachidis and Pitas [54] presented a novel method of image watermarking in which a circularly symmetric watermark is embedded in the DFT domain.
In addition, the proposed method is robust against the geometric rotation attacks
because the watermark is circular in shape with its center at image center.
15
(a) Embedder
(b) Extraction
Figure 1.4: General block diagram of transform domain-based image
watermarking system.
Discrete Cosine Transform (DCT)-based Method
Transform domain-based image watermarking methods possess a number of desirable properties as compared to spatial domain-based methods. Moreover, these
methods make it difficult for any intruder or unauthorized user to read or change
the watermark data. Since in the transform domain, embedded watermark is distributed over the area of the image after the inverse transformation. The DCT
domain-based method is divided into two groups namely; global-based and blockbased DCT image watermarking methods. The image watermarking methods that
rely on the global DCT approach, spread the watermark over the entire image.
On the other hand, block-based approach embeds the watermark as follows:
1. Divide the host image into non-overlapping blocks of 8 × 8.
2. Take the DCT to each of the block as mentioned in step 1.
3. Choose a specific block for watermark embedding by using certain criteria
like Human Visual System (HVS).
4. Choose coefficients for watermark embedding by using certain selection criteria like highest or lowest magnitude.
5. Cast the watermark data by modifying the selected coefficients; and
16
Table 1.5: Categories of transform domain-based image watermarking method.
S.No.
Category
Description
1.
DFT-based
Robust against image contrast operation. [49]
Robust against the geometric attacks. [50]
Robust against the rotation, scaling, and translation attacks.
However, this method is not robust enough against the cropping and compression attacks. [51]
Watermark embedded by modifying the mid frequency band of
DFT coefficients. Furthermore this method is robust against
JPEG and SPIHT compression attacks. [52], [53]
Circularly symmetric watermark is embed in the DFT domain
and concluded that method is robust against the geometric
rotation attacks. [54]
2.
DCT-based
Global DCT approach by exploiting the HVS and shows that
the method is robust against the geometric attacks like rotation, scaling, etc. [8], [15]
Block-based DCT method. DCT-based methods missing the
time and frequency information at the same time. [55], [56]
3.
DWT-based
Based on principle of “toral automorphism”. [31]
Known as “cocktail watermarking”and concluded that the
method is robust against the all possible watermarking attacks.
[57]
Presented a novel method for any size of images, which hides
watermark into the high-frequency sub-bands of DWT coefficients. [58]
Uses Daubechies-2 filter bank for transformation of host image
and show that the proposed method is robust to geometric,
filtering, and StirMark attacks. [59]
Uses Symlet-8 filter bank and shows that the method is robust
against various attacks. [60]
Uses Har wavelet for host image transformation. [61]
Uses Symlet-4 filter bank. [42]
4.
SPT-based
Robust against the various geometric attacks in comparison
with DWT-based method. [46]
Hybrid watermarking method using the SPT and SVD, this
method have good visual quality and resistance against several
attacks. [62]
Robust against the common signal processing and geometric
attacks like rotation. [63]
6. Take inverse DCT (IDCT) transform on each block.
In the DCT-based image watermarking method, most of the research is dedicated
to design the specific criteria for selecting the particular block and coefficients.
Cox et al. [15] proposed a robust image watermarking method using the global
DCT approach, which embeds the imperceptible watermark data into the host
image by exploiting the HVS. Koch et al. [8] reported a method for watermark
embedding having following steps:
1. Divide the host image into non-overlapping blocks of 8 × 8.
2. Take the DCT to each of the block as mentioned in step 1.
17
3. Choose the specific block by using the pseudo-random subset criteria.
4. A triplet of frequencies is selected from 1 of 18 predetermined triplets.
5. Cast the watermark data by modifying the selected coefficients, so that their
relative strengths encode a 1 or 0 value.
6. Take inverse DCT transform on each block.
In literature, various image watermarking methods have been proposed by using
DCT [55, 56, 64, 65, 66, 67, 68, 69]. Out of the existing DCT-based watermarking
methods, the block-based DCT method is widely used by researchers in the area of
watermarking. Lin et al. [69] find that the DCT-based methods are robust against
JPEG compression, but as robustness increases the quality of watermarked image
decreases. Moreover, DCT-based methods are not robust against the geometric
attacks like rotation, scaling, etc.
Discrete Wavelet Transform (DWT)-based Method
To achieve the robustness of image watermarking methods, discrete wavelet transform utilizes the spatial and frequency information of the transform data in multiple resolution. Recently, many image watermarking methods have been reported
which exploit the advantage of DWT over the DFT and DCT [40, 41, 42, 58, 59,
60, 70, 71, 72, 73, 74]. However, the performance of DWT-based method can be
further enhanced by exploiting the characteristics of HVS during the watermark
embedding stage. If a watermarking method can utilize the characteristics of the
HVS, then it is possible to embed watermark with more energy in a host image,
which makes watermark more robust. Although, the HVS model enhances the
imperceptibility and robustness of watermarking method, it suffers with the computational cost and complexity point of view. According to the HVS, the human
eye is less sensitive to noise in high resolution DWT bands having an orientation
of 450 . From this point of view, the DWT is a very useful transform as compared
to DFT and DCT, since it can be used as a computationally efficient version of
the frequency model for the HVS [70]. One of the reasons for the popularity of
DWT-based image watermarking method is that various multimedia standards
like JPEG2000, MPEG-4, etc. are based on the DWT. Hence, DWT decomposition can be exploited to make a real-time watermark application. DWT-based
image watermarking methods are capable to embed a fairly good quality of watermark and it can recover the watermark from watermarked image effectively.
The quality and robustness of DWT-based methods depend on the selection of
18
particular filter bank and decomposition level [61]. Voyatzis and Pitas [31] developed a robust method on the principle of “toral automorphism”, in which they
embed the binary logo watermark. Lu et al. [57] presented a method in which
they embedded dual watermarks with complement to each other. This method
is popularly known as “cocktail watermarking”. Furthermore, the result shows
that the proposed method is robust against all possible watermarking attacks.
Zhao et al. [75] reported a dual domain-based method for image authentication. In their method, authors utilized the DCT for watermark generation and
DWT for watermark casting. Agreste and Andaloro [58] presented a novel DWTbased watermarking method for any size of image which hides watermark into the
high-frequency sub-bands of DWT coefficient of host image. Later, Agreste and
Andaloro [59] reported another method which was the modified version of their
previous method by changing the filer bank by Daubechies-2, and concluded that
it is more robust to geometric, filtering, and StirMark attacks. Ghouti et al. [41]
selected balanced multi-wavelets filter bank for the data hiding and found that the
method is more robust against the various watermarking attacks. Furthermore,
Khelifi et al. [60] utilized the advantage of symlet-8 filter bank in their method.
Vahedi et al. [61] uses the Haar wavelet for message hiding. Moreover, Vahedi
et al. [42] exploited the advantage of symlet-4 filter bank to increase the quality
and robustness of watermarking method as compared to existing methods. In his
paper, they proposed a novel DWT-based method for color images by embedding
the binary watermark. For embedding the watermark, DWT-based methods use
three or higher level decompositions [39, 40, 42].
Steerable Pyramid Transform (SPT)-based Method
During the recent years, various image watermarking methods have been developed by using the DWT because it possess a number of desirable properties as
compared with the other transform-based methods. However, there are still rooms
for improvements in the field of watermarking. Although, DWT-based image watermarking methods have various advantages, it suffers in terms of recording the
directional information which is very important components for any digital image
processing operations [44, 47]. Therefore, scholars in this area looking for another reversible transform which possess all the properties of DWT. As a result,
researchers in this area propose various scale and directional image illustrations
during the years and results show that out of other representation, SPT having all
the advantages of DWT. Further, SPT is also capable in capturing the directional
information. Moreover, the results show that the SPT-based image watermarking
methods are more robust against the various geometric attacks in comparison with
19
DWT-based method [46]. Literature shows that the SPT keeps most of the advantages of DWT as its basis functions are confined to a small area in both space
and spatial-frequency. However, this recursive multi-scale & multi-directional decomposition improve the drawbacks of DWT like; it is aliasing free and capable to
generate any number of orientation bands as it is based on a category of random
orientation filters produced by linear grouping of a set of basis filters [46, 76].
Invariance, multi-resolution, and capture of multi-scale and multi-resolution constructions in the images are some of main properties of SPT which make it superior
in watermarking methods. Moreover, researchers concluded on the basis of the results that SPT-based methods of image watermarking are more robust against
the common signal processing and geometric attacks [45, 46, 47, 63]. There are
lot of scope of the research using SPT-based watermarking method because very
few SPT-based image watermarking methods [46, 62] have been reported till date.
Drira et al. [46] developed a SPT-based method and shows that it is resistant
to JPEG compression, additive noise, and median filtering. Hossaini et al. [62]
presented a novel hybrid watermarking method using the SPT and singular value
decomposition (SVD) and concluded that proposed method has good visual quality and resistance against several attacks.
Following section reviews the various optimization methods used to improve
the performance of image watermarking along with their constraints.
1.6.3
Optimization Methods in Image Watermarking
Previous section presented a state-of-the-art survey of different image watermarking methods using various domain like spatial and transform domain. Literature
survey reveals that the transform domain methods are more robust in comparison with spatial domain. In any watermarking method the aim is to maximize
the various parameters like transparency, robustness, capacity, etc. as given in
Section 1.3. Furthermore, the above mentioned parameters are the function of
strength factor of watermark to be added into the host image. However, all these
parameters are inversely related to each other. Therefore, to enhance the performance of the method, researchers must select the optimum value of strength
factor of watermark, so that all above mentioned parameters are maximized simultaneously. From this point of view, there is a room for the optimization methods
in the watermarking area to enhance the performance by optimally selecting the
value of strength factor. There are various optimization methods namely; genetic
algorithm (GA), artificial bee colony (ABC), differential evolution (DE), particle
swarm optimization (PSO), etc. which have been used to increase the quality and
20
robustness of watermarking methods [77]. During the recent years, many watermarking methods have exploited the advantages of various available optimization
methods [42, 55, 71, 78, 79, 80, 81, 82, 83, 84, 85]. The detailed survey of different
optimization methods has been presented by Karaboga and Akay [82] and Darwish
and Abraham [13]. A brief summary of the optimization methods used in image
watermarking methods is presented in Table 1.6.
Table 1.6: Categories of optimization methods in image watermarking.
S.No.
1.
Category
Details
Genetic Algorithm
Optimize 24 strength factor of watermark. [42]
Uses GA for search the optimum values of parameters. [81, 86]
Use GA to embed two bits of watermark data within each pixel
of host image. [87]
2.
Differential
Method
Evolution
Scale factors optimized using DE. [88]
Used a modified DE (self-adaptive differential evolution) algorithm for optimizing the scaling factors. [85]
Applied a DE optimization method, to search optimal scaling
factors. [89]
Use the DE to optimally design the quantization steps (QSs) for
calculating the strength of the watermark for achieving good
robustness and quality. [90]
3.
Artificial Bee Colony
Method
Uses the ABC method to optimize pixel by pixel embedding
at different frequency sub-band with DWT, to improves the
performance of proposed image watermarking method. [91]
Genetic Algorithm
Many researchers used GA in their method for optimizing the watermark strength
to enhance the overall performance. GA have been used by Vahedi et al. [42, 80] to
compute the optimum strength of watermark and result shows that it improves the
robustness of existing methods. Kumsawat et al. [81, 86] used GA in their methods
for searching the optimum values of parameters and the embedding strength to
improve the transparency and robustness of proposed method. Mohammed et
al. [87] proposed a novel method in which they used GA to embed two bits of
watermark data within each pixel of host image.
Differential Evolution Method
A detailed survey of DE method has been presented by Das et al. [92]. Vesterstrom and Thomsen [93] compared the performance of DE with PSO, and other
21
evolutionary algorithms (EAs), and reported that DE outperforms other considered methods. Aslantas [88] reported a method in which the singular values (SVs)
of the host image are manipulated to embed the watermark by using multiple
scaling factors. Further, scale factors are optimized using DE to enhance the
performance of proposed method. Later, Aslantas [94] exploits the DE method
for optimizing the scaling factor parameters to achieve maximum robustness and
transparency. Ali and Ahn [85] used a modified DE (self-adaptive differential evolution) algorithm for optimizing the scaling factors of watermark data to achieve
better robustness and quality in DWT-SVD based watermarking method. Further, Ali et al. [89] applied a DE optimization method to search optimal scaling
factors to improve the quality of watermarked image and robustness of the watermark. Lei et al. [90] proposed a method in which they used DE to optimally
design the quantization steps (QSs) for calculating the strength of the watermark
for achieving good robustness and quality.
Artificial Bee Colony Method
Artificial Bee Colony (ABC) optimization method is one of the most recently developed swarm-based algorithms. A very few watermarking methods have been
reported which utilizes the powerfulness of ABC for optimization in their methods
till date. Karaboga and Akay [82] compared ABC optimization method with GA,
PSO, etc. and concluded that the performance of ABC is better than other optimization methods. Recently, Akay and Karaboga [95] presented a state-of-the-art
survey and applications in the field of image processing. Sha et al. [91] uses the
ABC method to optimize pixel by pixel embedding at different frequency sub-band
with DWT to improve the performance of proposed image watermarking method.
Following section reviews the various color spaces used in image watermarking
method in order to enhance the performance parameters along with their constraints.
1.6.4
Color Spaces in Image Watermarking Method
At the outset, early image watermarking methods embed the watermark message
within the gray scale or color host image in the form of bits or bit stream and
further these bit streams are replaced by some pictorial shape representations
[39, 40]. In various multimedia applications namely; MPEG-1, MPEG-2, and
other MPEGs, the color images are the basic component. Hence, it is vital to
develop a watermarking method for color images. Although digital images are
22
available in color format, most of the watermarking methods embed the gray
scale or binary image watermark while very few work has been reported for color
watermark [96, 97, 98]. In color image processing, we cannot ignore the effects of
different color spaces used on the performance of the method. Furthermore, Vahedi
et al. [99] demonstrated the effects of different color spaces on the performance
of an image watermarking method. The color spaces are dividing into two classes
namely; correlated and uncorrelated color space. The summary of color spaces
used in image watermarking is given in Table 1.7.
Table 1.7: Categories of color spaces in image watermarking method.
S.No.
Category
1.
Correlated
Spaces
Comments
Color
Uses HSV, RGB, YUV and HSI color models respectively
in image watermarking method. [34, 58, 100, 101]
They uses HSI color space in DWT-based watermarking
method. [42]
2.
Uncorrelated
Spaces
Color
Uncorrelated color models Lab and Lαβ are used.[102]
Embeds the color watermark in Lab color space. [96]
Correlated Color Spaces
In a correlated color spaces, a color image is decomposed into three semi-independent
images in which change in one component may affect the other two components
of the images. In literature, various correlated color spaces namely; RGB, Y Cb Cr ,
YIQ, HSI, HSV, etc. are being used in image watermarking methods by the researchers. Methods proposed by researcher in [34, 58, 100, 101] uses HSV, RGB,
YUV, and HSI color models respectively. Vahedi et al. [99] reported a work
dedicated to the performance of DWT-based watermarking method on different
color spaces like RGB, Y Cb Cr , YIQ, HSI, and HSV. In which they concluded
that HSI color space outperforms other considered spaces. Golea et al. [97] presented the SVD-based RGB color image watermarking for embedding the color
RGB watermark into the RGB host image. Recently, Su et al. [98] presented QR
decomposition method for embedding the RGB color watermark into RGB host
image. Vahedi et al. [42] presented a DWT-based watermarking method in which
they used HSI color space to enhance the robustness and transparency.
Uncorrelated Color Spaces
Most of the image watermarking methods reported so far have used the correlated
color spaces. However, correlated color spaces impose the constraints to use only
23
one color channel at a time for casting the watermark data [103]. As a consequence,
researchers in this area proposed various color co-ordinate illustrations during the
recent years to removes the dependency of three decomposed images. Such class
of color space is called as uncorrelated color space. There exist some uncorrelated
color models such as Lab, Lαβ, uncorrelated color space (UCS), etc. which may
be used in color image watermarking to increase the robustness and quality by
using all the color image components of host and watermark images [43, 102,
104]. There is a lot of scope of the research using uncorrelated color spacesbased watermarking method because very few work has been reported till date.
Chou and Wu [96] embedded the color watermark in Lab color space using less
computationally complex spatial-domain color image watermarking method.
1.7
Challenges in Image Watermarking
Though many image watermarking methods have been proposed and demonstrated significant contribution, there are still some challenges which need to be
addressed. One of the main challenges of the watermarking problem is to achieve a
better trade-off between robustness, transparency, capacity, and security. In order
to address above mentioned issue (i.e. the trade-off) to achieve a better performance, many researchers presented solutions for this issue in their work. However,
improvements are required to fulfill the expectation of the industry. This section
overviews some of the crucial challenges of image watermarking.
1.7.1
Use of Color Watermark
Most of the work during the past decade in this area have been reported for
protection of the color or gray scale image (host) by embedding the gray scale
or binary image watermark. To embed a binary or gray scale image, one has to
convert it from color image because in nature color images are available. However,
very few methods have been reported which embed color watermark for protection
of images [96, 97, 98]. From this point of view, there is still a lot of scope for
improvements in the field of image watermarking to embed a color watermark
into the color host image.
1.7.2
Color Spaces
In color image watermarking, we are dealing with color images for host and watermark. To read a color image there are various color spaces/ coordinates available
in the literature as given in Section 1.6.4. Therefore, researchers are use different
24
color spaces to improve the performance of the method. Robustness and transparency of any method depend on how an input host is decomposed into three
color channels and also the decomposed images are independent with each other
or not. In other words, performance parameters of a color image watermarking
depend on the color space used in the method [99]. To consider above facts, there
is a lot scope to improve the performance of method by using a suitable color
space which decompose an image into three independent images. Fortunately, a
class of color spaces known as uncorrelated color space, generate three independent images. However, a very few method has been reported which exploits this
class of color coordinate.
1.7.3
Transform Method
Though various reversible transform methods, as given in Section 1.6.2, have been
used in the reported image watermarking methods, still there is scope for improvements of image watermarking methods by using some newly introduced transforms
(like SPT and other) which show better performance as compare to existing methods.
1.7.4
Optimization Method
A better trade-off is required between the parameters given in Section 1.3 to enhance the performance of the watermarking method. Furthermore, better trade-off
between the parameters depend on how optimally select the embedding parameters (like strength factor of watermark). To achieve this objective, there are
various optimization methods which have been used by the researchers as given
in Section 1.6.3. From this point of view, there is a room for improvements in
the performance of image watermarking method by using some newly developed
optimization methods like ABC, DE, etc.
1.7.5
3-D Watermarking
A recent challenge in watermarking is for protection of the 3-D objects or models
against the illegally utilizations which was introduced by Ohbuchi [105]. This
type watermarking is called “3-D watermarking”. Recently, 3-D watermarking
have been widely used in virtual reality, medical imaging, video games, computer
aided design, etc. This is considered as a new kind of multimedia that has scored
an increasing success. A very few work has been reported to protect the 3-D
models against the illegal utilizations till date, though it has been widely utilized
25
in the entertainment industry.
1.8
Scope of the Thesis
The challenges discussed above have not been fully resolved. Therefore, there is
a need to design and develop a robust image watermarking method. The main
contributions in this thesis are five-fold. It first aims to pre-process the input color
images (host and watermark) by transforming it into UCS color space. Second,
four efficient transform-based image watermarking methods for color images have
been proposed to enhance the transparency and robustness of method. Third,
three optimization methods have been used to improve the performance of proposed methods by optimally selecting the strength factors of watermarks and then
post-processed the watermarked coefficients to reconstruct the watermarked image. Fourth, applying various watermarking attacks in order to test the proposed
method on various benchmark/ validation parameters like composite-peak-signalto-noise ratio (CPSNR), structural similarity (SSIM), and normalized correlation
(NC). Finally, an image watermarking method is designed and developed using
the proposed methods for protection of color images.
Including this introductory chapter, the rest of the thesis is organized in the
following five chapters.
Chapter 2 discusses literature survey of the existing methods for image watermarking using DWT which embed gray-scale watermark image for the protection
of color host images. It then briefly describes the various preliminaries like DWT
method, UCS, and GA followed by the mathematical concept of pre-processing of
input images, embedding, post-processing, and extraction of watermark images.
Moreover, GA has been used to optimize the watermark strength during the embedding phase. Further, the testing of the proposed method is done against the
various watermarking attacks. The results of the proposed method are compared
with existing methods and finally discussion is presented.
Chapter 3 reviews of various available SPT-based image watermarking methods which used gray-scale watermark image for the protection of color host images. Then, brief descriptions of various preface such as SPT method, UCS, and
GA are presented followed by the mathematical concept of pre-processing of input images, embedding, post-processing, and extraction watermark images. The
proposed method also uses GA to optimize the watermark strength during the embedding phase. The results of the proposed method are compared with existing
methods and the method proposed in Chapter 2.
Chapter 4 deals with the protection of color images by embedding the color
26
watermark by using the DWT and ABC methods. This chapter presents a survey
of the existing methods for color image watermarking using DWT followed by
preliminaries. It then describes the pre-processing of input images, embedding,
post-processing, and extraction of watermark images. Then, three optimization
methods have been used to optimize the watermark strength during the embedding
phase followed by the testing of the method. The results of the proposed method
are compared with existing methods.
Chapter 5 proposes a color image watermarking method by casting the color
image watermark into the color host image by using the SPT and DE. A brief
discussion about the SPT and DE methods have been presented followed by the
procedure for embedding and extraction phase. In the proposed method, DE
has been used to optimize the watermark strength during the embedding phase
followed by the testing under various attacks. The results of the proposed method
are compared with existing methods and proposed method in Chapter 4.
The last chapter summarizes the key findings, main contributions of the
thesis, and possible scope for future research in this area.
27
Chapter 2
Digital Image Watermarking using Discrete Wavelet Transform on Gray-Scale
Watermark Image
2.1
Introduction
Two embedding-domain have been used for digital image watermarking methods,
namely; spatial and transform domain as mentioned in Section 1.3. The detailed
survey related to the image watermarking methods is given in the Section 1.6. An
image watermarking method based on the transform domain is more robust than
the spatial domain [15]. Researchers suggested that DWT-based methods of image
watermarking are more robust against the common signal processing and malicious
attacks [51, 106, 107] as compared with the DFT and DCT-based methods. DWTbased methods are capable in embedding a better quality of watermark and also
can recovered it from the watermarked image effectively. Therefore, this chapter
focuses on the protection of color images from its illegal utilization using a DWTbased digital image watermarking method.
During past decade, many image watermarking methods [40, 41, 42, 58, 59,
60, 70, 71, 72, 73] have been reported which utilized the advantages of DWT over
the DFT and DCT [74]. DWT-based methods are more robust because it is more
close to the frequency model for the HVS [70]. The characteristics of HVS model
are used by various researcher in image watermarking to enhance the transparency
and robustness. Kundur et al. [39] exploit HVS model to generate a visual cover
for multi-resolution-based image watermarking method. Later, Reddy et al. [40]
and Ghouti et al. [41] uses the advantages of HVS in their methods. Furthermore,
the performance of the DWT-based methods depend on the parameters namely;
selection of filter bank, decomposition level, and selection of embedding decomposed coefficients [42]. Therefore, all the work reported in this area using DWT
are based on the changing the above mentioned parameters to show the better
robustness and transparency of watermarking method. Vahedi et al. [42] exploit
the advantage of symlet-4 filter bank to increase the quality and robustness of watermarking method as compared to existing methods. In his paper, they proposed
a novel DWT-based method for color images by embedding the binary watermark. Vahedi et al. [42] showed that three level decomposition with all the four
sub-spaces namely approximation, horizontal, vertical, and diagonal along with
symlet-4 filter bank provides better results for image watermarking. Therefore, in
this method DWT has been used with three level of decomposition and symlet-4
filter bank for embedding the watermark. Moreover, to increase the quality and
robustness of watermarking methods, this method utilizes the capabilities of GA
to optimize the watermark strength.
Generally, the researchers used RGB, Y Cb Cr , YIQ, HSI, HSV, etc., color space
models for host image in their digital watermarking method. The above mentioned
color models are correlated i.e. the image components are not independent and
change in one component may affect the other components of the image. This
imposes the constraints for the researchers who used correlated color host image
to use only one color component at a time for embedding the watermark data.
However, there exist some uncorrelated color models such as Lab, Lαβ, UCS,
etc. [102, 108], which may be used in color image watermarking to increase the
robustness and quality by using all the color image components of host images.
Saraswat and Arya [108] used UCS for color transfer of images and observed that
UCS outperforms the other uncorrelated color spaces.
Therefore, due to the limitations of correlated color spaces and powerfulness of
DWT, this chapter proposes a DWT-based image watermarking method using uncorrelated color space (UCS). Further, GA has been used to optimize the strength
factor of the proposed watermarking method. In this chapter Section 2.2 presents
the various preliminaries used. Proposed method and performance improvements
using GA to the proposed method have been discussed in Section 2.3. Further,
experimental results and the conclusion of the chapter are presented in Section
2.4 and 2.5 respectively.
2.2
Preliminaries
In this chapter, the proposed method uses DWT, UCS and GA methods for digital
image watermarking. Therefore, following section describe the functions of these
methods in brief:
30
2.2.1
Discrete Wavelet Transform (DWT)
DWT is frequently used in various image processing applications namely; compression, watermarking, etc. DWT is a sampled version of continuous wavelet transform. The main advantage of DWT is that it maintains both frequency and time
information at the same time which was missing in DFT. The transform based on
small sinusoidal waves of varying frequency and limited duration is called wavelet.
DWT is used to decompose the input image into sub-images of diverse spatial
direction (i.e. horizontal, vertical, and diagonal) and independent frequency area
[40, 41]. Transformation of an image from the spatial domain to DWT domain
by one level decomposes the input image into four different frequency bands in
which one is the low frequency and remaining three are the high frequency bands.
These bands are represented as LL (approximation detail of image), HL (horizontal detail of image), LH (vertical detail of image), and HH (diagonal detail of
image) respectively. Magnitude of DWT coefficients is larger in the lowest bands
(LL) at each level of decomposition and is smaller for other bands (HH, LH, and
HL). Therefore, after one level decomposition, the further decomposition of given
image is done using only LL sub-space which is also decomposed into four distinct
frequency bands as mention above. Figure 2.1 shows the two dimensional image
of size 512 × 512 before and after the three level of DWT decomposition with their
sub-spaces size. Since, a two dimensional image has been used, it needed a 2-D
wavelet transform. The 2-D DWT is implemented as a 1-D row transform followed
by a 1-D column transform. The 2-DWT transform coefficients for input image
function f (n1 , n2 ) of size N1 × N2 are calculated using Eq. (2.1) and (2.2).
N
1 −1 N
2 −1
1
f (n1 , n2 )ϕj0 ,k1 ,k2 (n1 , n2 )
Wϕ (j0 , k1 , k2 ) = √
N1 N2 n =0 n =0
(2.1)
N
1 −1 N
2 −1
1
=√
f (n1 , n2 )ψji0 ,k1 ,k2 (n1 , n2 )
N1 N2 n =0 n =0
(2.2)
1
Wψi (j0 , k1 , k2 )
1
2
2
here, j0 is the starting scale and i = {H, V, D} indicates the directional index of
wavelet function. Eq. (2.1) calculates the approximation coefficient while (2.2)
calculates the other detail coefficients. 2-D scaling function ϕ and wavelet function
ψ i , used in Eq. (2.1) and (2.2), may be calculated through the separable 1-D filter
having the impulse response hϕ (−n) and hφ (−n) respectively. Sub-spaces LL, HL,
LH, and HH are calculated by putting the values of ϕ(n1 , n2 ) and ψ i (n1 , n2 ) from
31
the Eq. (2.3)-(2.6) into Eq. (2.1) and (2.2).
ϕ(n1 , n2 ) = ϕ(n1 )ϕ(n2 )
(2.3)
ψ H (n1 , n2 ) = ψ(n1 )ϕ(n2 )
(2.4)
ψ V (n1 , n2 ) = ϕ(n1 )ψ(n2 )
(2.5)
ψ D (n1 , n2 ) = ψ(n1 )ψ(n2 )
(2.6)
Figure 2.1: Three level decomposition layout of an image.
To reconstruct the image from the DWT coefficients, inverse DWT (IDWT) is
calculated as follows :
f (n1 , n2 ) = √
1
Wϕ (j0 , k1 , k2 )ϕj0 ,k1 ,k2 (n1 , n2 )
N1 N2 k k
1
1
+√
N1 N2
2
∞ i=H,V,D j=0 k1
(2.7)
Wψi (j0 , k1 , k2 )ψji0 ,k1 ,k2 (n1 , n2 )
k2
To embed the watermark in DWT-based decomposed image, the transform coefficients of DWT are modified by watermark. Since, the low frequency band (LL) of
DWT decomposed image is similar to the original image, most of the information
or energy of original image lies in this frequency band. In order to maintain the
quality of watermarked image, this low frequency or approximation detail must be
preserved and maintained the robustness of embed watermark. Therefore, it is the
trade-off between the robustness and quality that at which extent the transform
coefficients are to be modified in order to optimize the overall method.
32
2.2.2
Uncorrelated Color Space (UCS)
The quality of the color image watermarking methods depends on how images
are split into three color channels, i.e. which color space was chosen. For better
quality, color space must be uncorrelated which makes the three color channels
semi-independent [103, 108] and may be used for embedding the watermark. This
method uses the recently developed UCS proposed by Liu [109]. UCS is derived
from RGB color space using principal component analysis (PCA). UCS uses a
linear transformation, WU ∈ R3×3 , of the RGB color space to uncorrelate the
component images as shown in Eq. (2.8) [109];
⎡
⎤
⎡
⎤
U(x, y)
R(x, y)
⎢
⎥
⎢
⎥
⎣C(x, y)⎦ = WU ⎣G(x, y)⎦
S(x, y)
B(x, y)
(2.8)
The WU is calculated by factorizing the covariance matrix C using PCA in the
following form [109]:
C = WUt ΛWU
(2.9)
here WUt and Λ are the orthogonal eigenvector matrix and diagonal eigenvalue matrix with diagonal elements in a decreasing order, respectively. Saraswat and Arya
[108] used UCS for color transfer of images and observed that UCS outperforms
the other uncorrelated color spaces.
2.2.3
Genetic Algorithm (GA)
Genetic algorithm (GA) belongs to the class of evolutionary algorithms and is
an ingredient of artificial intelligence. These algorithms are capable to encode a
solution to the different engineering problems. Furthermore, to achieve a better
solution to the problem these algorithms use methods which are stimulated by
nature namely; inheritance, reproduction, mutation, and crossover. GAs were
reported by the Holland [110, 111].
Terminology
The term used in GA are as follows:
• Search space : the space of all possible solutions.
• Chromosome : it contains the solution in form of genes.
• Population : a set of individuals/chromosomes.
33
• Generation : the procedure of evaluation, reproduction, crossover and mutation.
• Fitness : the value assigned to an individual based on how far or close it is
from the desired solution.
The pseudo-code of the GA is shown in Algorithm 2.1 and the flow chart is
depicted in Figure 2.2.
Algorithm 2.1 Genetic Algorithm
Genetic representation of solution to the problem
Create and initialize the population individuals;
Evaluate the fitness of each individual in population
while Termination criteria is not satisfied do
1. Reproduction: Select the individuals with greater fitness for reproduction.
2. Crossover: Reproduce new individuals through crossover.
3. Mutation: Apply probabilistic alteration or modification on new individuals.
4. Form a new population with these offsprings.
end while
Figure 2.2: Genetic Algorithm Flow Chart.
In GA, there are three operators: reproduction, crossover, and mutation. Initially, a population is generated randomly with uniform distribution followed by
reproduction, crossover, and mutation operators to generate a new population.
Offspring vector generation is a crucial step in GA process. The two operators,
34
crossover and mutation are used to generate the offspring vectors. The reproduction operator is used to select the best vector between offspring and parent for the
next generation. GA operators are explained briefly in the following sections.
Reproduction
Reproduction or selection operator selects the individuals or chromosomes to
crossover and to generate offsprings. The selection is crucial and important step
and it is based on the principle of the best one to be survive and generates new
offsprings. To select the best individual for the optimum solution of given problem, the fitness function is formulated. This function shows the closeness of a
current result to the desired result. There are various techniques for reproduction
operator namely; Roulette-wheel selection, tournament selection, rank selection,
steady-state selection, Boltzmann selection, and scaling selection.
Crossover
Crossover operator selects genes from parent chromosomes, combines them and
produces a new children or offspring. The idea behind this operator is that new
offspring may have better characteristics than both of the parents to achieve the
desired results.There are various techniques for crossover operator namely; singlepoint crossover, two point crossover, uniform crossover, and arithmetic crossover.
Mutation
Mutation operator modifies or alter more than one gene values in an individual.
The advantage of the mutation operator is that it can produce new genes values
in the individual or chromosomes which is not present in a given search space.
Moreover, these new genes can produce the better solution for given problem.
2.3
Proposed Method
The proposed method explores the advantages of uncorrelated color space over
the correlated color space to improve the performance of watermarking methods
in terms of quality and robustness. It implants the gray-scale watermark image
into the color host image by modifying the decomposed wavelet coefficients of host
image. The watermark image is embedded in each color channel of host image to
increase the reliability during the recovery process and protect against the common
signal processing attacks. The proposed method consists of five phases namely;
preprocessing of host and watermark image, watermark embedding, image post
35
processing, extraction of watermark, and performance improvement using GA.
The basic structural design of the proposed method is shown in Figure 2.3. The
details of each phase of the proposed method is described in the following sections.
Figure 2.3: Structural design of proposed method.
Image Pre-processing
In image preprocessing, host RGB color image (H) is transformed into UCS color
space [109], using Eq. (2.10) which produces three independent image components
36
namely HU , HC , and HS .
⎡
⎤
⎤
⎡
HU (x, y)
HR (x, y)
⎢
⎥
⎥
⎢
⎣HC (x, y)⎦ = WU ⎣HG (x, y)⎦
HS (x, y)
HB (x, y)
(2.10)
The WU is calculated by factorizing the covariance matrix C using PCA in the
following form [109]:
(2.11)
C = WUt ΛWU
where WUt and Λ are the orthogonal eigenvector matrix and diagonal eigenvalue
matrix with diagonal elements in a decreasing order, respectively. The watermark
image is divided into 16 non-overlapping sub-areas as shown in Figure 2.3. These
sub-areas of watermark image are then re-arranged or scrambled by some predefined sequence or key to introduce one more level of security and enforced the
user to use the key for the extraction of the image from watermarked image.
Further, third level of decomposition is applied on each component of host image
using symlet-4 wavelet function. The resultant third level wavelet coefficients,
Hk3 , is chosen for embedding process, k = {LL, HL, LH, HH}.
Watermark Embedding
After preprocessing of the images, the scrambled watermark is embedded into the
DWT coefficients of host image. The third level decomposition coefficients of host
image are HLL3 (x, y), HHL3 (x, y), HLH3 (x, y), and HHH3 (x, y), representing the
approximation, horizontal, vertical, and diagonal details of 3-DWT decomposed
host image respectively. In order to increase the reliability and robustness of
proposed method against the malicious attacks, it is desired to hide the watermark
image in all the color components of host coefficients. Therefore, approximation
details of the third level coefficients are divided into 16 non-overlapping areas.
Now, the scrambled watermark is embedded into the approximation details of the
third level decomposed host image coefficients in its all color channels by using
Eq. (2.12).
HWk3 (x, y) = Hk3 (x, y) + α(r)Wp (x, y)
k = {LL, HL, LH, HH}
p = {1, 2, 3, ..., 16}
r = {1, 2, 3, ..., 16}
(2.12)
where, k represents the sub-areas of host image, p is the intended block of watermark, and α denotes the strength of modification done in the host coefficients.
37
Post-processing
After embedding the watermark image into corresponding DWT host image coefficients, inverse DWT (IDWT) is taken for watermarked host image coefficients
which returns watermarked image in UCS color space. Finally UCS watermarked
image is reconverted into RGB color space.
Watermark Extraction
The watermark extraction process requires the watermarked image and the predefined key. To extract the watermark, RGB watermarked image is transformed
into UCS color space and then 3-DWT decomposition using symlet-4 wavelet filter
bank on each channel is applied. To gathered the fraction watermarked image,
a reverse process of embedding ia applied as shown in Figure 2.3 and extract
the watermark image from this fraction using Eq. (2.13). Finally, the scrambled
watermark image is rearranged to its original sequence.
Ŵp (x, y) =
Hk3 (x,y)−HWk3 (x,y)
α(r)
k = {LL, HL, LH, HH}
p = {1, 2, 3, ..., 16}
r = {1, 2, 3, ..., 16}
(2.13)
Performance Improvement using GA
The watermarked image HWk3 is subjected to various watermarking attacks which
degrade the performance of watermarking methods. There are mainly two parameters namely; quality and robustness which must be maximized for a watermarking
method, but these parameters are inversely related with each other i.e. if quality increases, robustness suffers and vice-versa. In this method, optimum values
of these parameters are dependent on the suitable values of strength factor (α).
Therefore, this method uses GA optimization method for selecting the values of
α which optimizes the quality and robustness in terms of fitness function. There
are 16 strength factors used in this method whose optimized values are calculated
38
by minimizing the following fitness function using GA [42].
90%
100
F itness =
[1 − NCjpg(Q) ]
+2×
CP SNR
Q=60%
+
50%
[1 − NCjpg(Q) ] +
+
[1 − NCscale(i) ] +
i=1
+
4
[1 − NCf ilter(i) ]
(2.14)
i=1
Q=10%
3
3
3
[1 − NCnoise(i) ]
i=1
[1 − NCcrop(i) ] + [1 − NCrotation ]
i=1
where, CPSNR is composite peak signal-to-noise ratio, and NC is normalized
correlation. These vales are calculated by applying the mentioned attacks on watermarked image. The GA method is executed for 200 iterations to calculate the
optimum values of strength factors (α).
2.4
Experimental Results
To compare the performance of proposed and considered image watermarking
methods, two popularly used 24-bit RGB color images namely lena and mandrill
each of size 512 × 512 are used as host images and two gray scale images namely;
RTU logo and aeroplane image each of size 64 × 64 are used as watermark images
to embed them in the host images and are shown in Figure 2.4. All the considered
images have been taken from USC-SIPI image database [112] except RTU logo
which is taken from Rajasthan Technical University, Kota, India.
(a) Lena
(b) Mandrill
(c) RTU logo
(d) Aeroplane
Figure 2.4: (a)-(b). RGB host images and (c)-(d). Gray scale watermark images.
In the pre-processing step of the proposed method, host images are converted
into UCS color space followed by third level decomposition of host images and
scrambling of watermark images. One of the possible scrambled watermark image
39
is shown in Figure 2.3. Now, the watermark images are embedded into each of the
considered host images.
The performance of the proposed methods is compared with Vahedi et al. [42] who
also used DWT with correlated color spaces and exploits the powerfulness of GA to
increases the quality and robustness of image watermarking method. To compare
the objective test for quality measures of resultant watermarked images using
proposed method and considered methods, CPSNR [42] performance parameters
are calculated and depicted in Table 2.1. From Table 2.1, it is validated that
both the methods maintain the quality of the watermarked image in terms of
CPSNR (> 35dB). However, the proposed method shows lower values of CPSNR
as compared to other considered method due to the hiding of complete watermark
more than once and into the approximation detail of host image.
Table 2.1: Comparison of CPSNR values of watermarked images resultant from
proposed and considered method.
Used Watermark
Watermarked
images
Vahedi et al. [42]
Proposed
Method
1.
RTU Logo
Lena
36.74
35.92
2.
RTU Logo
Mandrill
35.85
35.67
3.
Aeroplane
Lena
36.32
35.61
4.
Aeroplane
Mandrill
35.42
35.59
S.No.
The robustness of the proposed method has been tested by applying different
attacks on the watermarked images. This method consists of filtering (mean,
median, wiener), noise (gaussian, poisson, salt and pepper), JPEG compression,
rotation, scaling, and cropping attacks. The attacks are applied on all the four
watermarked images embedded with RTU logo and aeroplane watermark images.
The comparison of NC values after applying the attacks on watermarked images
embedded with RTU logo and aeroplane image are depicted in Table 2.2. From
Table 2.2, it is observed that the robustness of watermarked images embedded
with RTU logo have higher values of NC as compared to aeroplane image due
to the coarseness of aeroplane image as compared to RTU logo. Moreover, the
similar performance of the proposed method can be observed from Figure 2.5 and
Figure 2.6 where the extracted watermark images are RTU logo and Aeroplane
images respectively, for all the considered attacks. Therefore, it is validated from
the results that the proposed method produces high quality and better robust
watermarked images and can be utilized for content authentication to protect the
copyrighted images. The comparative results show that the proposed method
outperforms other method for all the considered attacks.
40
41
0.73
0.88
0.70
0.84
0.92
Median filtering (3 × 3)
Wiener filtering (3 × 3)
Mean filtering (3 × 3)
Gaussian noise (0.006)
Poisson noise
Salt and pepper noise
JPEG compression (10%)
JPEG compression (90%)
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
0.59
0.71
0.81
0.92
0.95
0.95
0.94
0.68
0.92
0.84
0.83
Vahedi
et
al.
[42]
Attacks
S.No.
0.62
0.74
0.84
0.96
0.99
0.99
0.98
0.96
0.88
0.71
0.96
0.88
0.87
0.99
0.92
0.76
Proposed
Method
lena
0.58
0.69
0.79
0.89
0.92
0.92
0.91
0.89
0.82
0.66
0.89
0.82
0.81
0.68
0.85
0.71
0.61
0.73
0.83
0.94
0.97
0.97
0.96
0.94
0.86
0.70
0.94
0.86
0.85
0.97
0.90
0.75
Proposed
Method
Mandrill
Vahedi
et
al.
[42]
RTU Logo
0.56
0.68
0.77
0.87
0.90
0.90
0.89
0.87
0.80
0.65
0.87
0.80
0.79
0.67
0.84
0.70
Vahedi
et
al.
[42]
0.61
0.73
0.84
0.95
0.98
0.98
0.97
0.95
0.87
0.70
0.95
0.87
0.86
0.98
0.91
0.75
Proposed
Method
lena
0.55
0.66
0.75
0.85
0.88
0.88
0.87
0.85
0.77
0.63
0.85
0.77
0.77
0.65
0.81
0.67
Vahedi
et
al.
[42]
0.60
0.72
0.82
0.93
0.96
0.96
0.95
0.93
0.85
0.69
0.93
0.85
0.84
0.96
0.89
0.74
Proposed
Method
Mandrill
Aeroplane Image
Table 2.2: Comparison of robustness in terms of NC values obtained after applying attacks on the watermarked images.
Extracted RTU logo Watermark Image After Attacks
Median (3x3)
Wiener (3x3)
Mean (3x3)
Gaussian (0.006)
JPEG (10%)
JPEG (90%)
Poisson
Salt & Pepper
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
Figure 2.5: Extracted RTU logo watermarks by proposed method after applying
considered attacks.
42
Extracted Aeroplane Watermark Image After Attacks
Median (3x3)
Wiener (3x3)
Mean (3x3)
Gaussian (0.006)
JPEG (10%)
JPEG (90%)
Poisson
Salt & Pepper
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
Figure 2.6: Extracted Aeroplane image watermarks by proposed method after
applying considered attacks.
43
2.5
Results and Discussions
This chapter proposes a DWT-based image watermarking method for protecting
the color host image by embedding gray-scale image using UCS and GA. The
use of UCS color space increases the effective utilization of all color channels
of host image which is not feasible in correlated color spaces while GA is used
for optimizing the strength factors to improve the quality and robustness of the
proposed method. The results validate that the proposed method is better than
other method for all the considered performance parameters. In other words, the
UCS color space is outperforms than the correlated color space utilized by Vahedi
et al. [42]. Therefore, it is concluded that the proposed method has high quality
and robust results and can further be used for protection of the copyrighted images.
The next chapter uses the SPT-based method to embed the gray-scale watermark image into color host image.
44
Chapter 3
Digital Image Watermarking using Steerable Pyramid Transform on Gray-Scale
Watermark Images
3.1
Introduction
Previous chapter presented an image watermarking method, which is based on
DWT and further GA has been exploited to optimize the performance of method.
In addition, the proposed method has been compared with existing method and
results shows that the proposed method outperforms. Although, proposed method
in previous chapter working well, there is still room for improvements in the field
of image watermarking by applying some latest developed transform as mentioned
in section 1.6.1.
The imperfect ability of DWT in capturing directional information, which is necessary components for image perception, processing, and reconstruction [47], causes
researcher to find alternative transform having all the advantages of DWT and
simultaneously removes all disadvantages. Fortunately, researcher come up several scale and directional image illustrations and results show that out of other
representation SPT outperforms among the class [46]. Researchers suggested that
SPT-based methods of image watermarking are more robust against the common
signal processing and geometric attacks [45, 46, 47, 63]. Literature shows that the
SPT keeps most of the advantages of DWT as its basis functions are confined to
a small area in both space and spatial-frequency. However, this recursive multiscale & multi-directional decomposition improve their drawbacks like; it is aliasing
free and capable to generate any number of orientation bands as it is based on
a category of random orientation filters produced by linear grouping of a set of
basis filters [46, 76]. Invariance, multi-resolution, and capture of multi-scale and
multi-resolution constructions in the images are some of main properties of SPT
which make it superior in watermarking methods.
Therefore, in this work SPT has been used with three level of decomposition
in place of DWT for embedding the watermark. Moreover, GA method is also
used for optimizing the strength factor of watermark to get better quality and
robustness as used in the previous method discussed in Chapter 2. In the similar
fashion of previous method described in Chapter 2, the host images and watermark
images have been converted to UCS color space before embedding.
Rest of the chapter is organized as follows. Section 3.2 presents the SPT used
in the proposed method while GA and UCS have already been described in Section
2.2. In Section 3.3, proposed method has been discussed followed by experimental
results and conclusions in the successive sections.
3.2
Steerable Pyramid Transform (SPT)
The earliest successful multi-scale, multi-orientation decomposition of an image
is SPT which has been proposed by Freeman and Adelson [44, 45]. The SPT
is a wavelet-like illustration whose investigation functions are scaled and rotated
editions of a single directional wavelet. Steerability is the property in which fundamental wavelets can be rotated to any orientation by appearing appropriate linear
groups of a primary set of equiangular directional wavelet components [113]. This
property is utilized in the SPT for finding the direction of basis function of an
image [76]. SPT decomposes the input image into a set of sub-bands of a variety
of orientations. Figure 3.1 depicts the decomposition block diagram of an input
image, consisting of the three kind of filters; low-pass (L0 ), high-pass (H0 ), and
bank of band-pass (B0 , ..., Bk ) filters. Here k is the order of basis functions of the
steerable pyramid and k + 1 are the orientations. The input image is sub-divided
into the high and low-pass sub-bands by the high and low pass filters respectively.
Every low-pass sub-band is again sub divided into the k + 1 oriented sub-bands
and a low pass sub-band. Finally, sub-sampled are created by a factor 2 and then
further decomposition is performed.
3.3
Proposed Method
The proposed method uses the uncorrelated color space to improve the quality
and robustness of watermarking methods. This method embeds the gray scale
watermark image into the color host image by modifying the decomposed SPT
coefficients of host image. The watermark is added in each color channel of host
image which increases the reliability of extraction process and protection against
46
Figure 3.1: Block diagram for steerable pyramid decomposition of an image.
the common signal processing attacks. The proposed method is divided into five
phases namely, preprocessing of host and watermark images, watermark embedding, image post processing, extraction of watermark, and performance improvement using GA. Figure 3.2 shows basic structural design of the proposed method.
The details of each phase of the proposed method is described in the following
sections.
Image Pre-processing
Image preprocessing, changes the host RGB color image (H) into UCS color image
[109], using Eq. (3.1) and generates three independent image components namely
HU , HC , and HS .
⎡
⎤
⎤
HR (x, y)
HU (x, y)
⎢
⎢
⎥
⎥
⎣HC (x, y)⎦ = WU ⎣HG (x, y)⎦
⎡
HS (x, y)
(3.1)
HB (x, y)
here, WU is calculated by factorizing the covariance matrix C using principal
component analysis (PCA) in the following form [109]:
C = WUt ΛWU
47
(3.2)
Figure 3.2: Structural design of proposed method.
where WUt and Λ are the orthogonal eigenvector matrix and diagonal eigenvalue
matrix with diagonal elements in a decreasing order, respectively. Further, 16
non-overlapping sub-areas are generated from watermark as shown in Figure 3.2.
These sub-areas of watermark are then re-arranged or scrambled by some predefined sequence or key. This increases the level of security and enforces the user
to use the key for the extraction of the watermarked image. Further, third level
of decomposition is applied on each component of host image. The resultant third
48
level SPT coefficients, Hk3, is chosen for embedding process, k = {1, 2, 3, ..., 16}.
Watermark Embedding
After the preprocessing step, the scrambled watermark is embedded into the SPT
coefficients of host image (Hk3). In order to increase the reliability and robustness of proposed method against the malicious attacks, it is desired to hide the
watermark image in all the color components of host coefficients. Therefore, low
frequency details of the third level coefficients are divided into 16 non-overlapping
areas. Now, the scrambled watermark is embedded into the low frequency details
of the third level decomposed host image coefficients in its all color channels by
using Eq. (3.3).
HWk3 (x, y) = Hk3 (x, y) + α(r)Wp (x, y)
k = {1, 2, 3, ..., 16}
p = {1, 2, 3, ..., 16}
r = {1, 2, 3, ..., 16}
(3.3)
where, k represents the sub-areas of host image, p is the intended block of watermark, and α denotes the strength of modification done in the host coefficients.
Post-processing
After embedding the watermark image into corresponding SPT host image coefficients, inverse SPT (ISPT) is taken for watermarked host image coefficients which
returns watermarked image in UCS color space. Finally UCS watermarked image
is reconverted into RGB color space.
Watermark Extraction
The watermark extraction process needs the watermarked image and the predefined key, to extract the watermark. RGB watermarked image is transformed
into UCS color space and then SPT decomposition on each channel is applied.
To gathered the watermarked image, a reverse process of embedding is applied
as shown in Figure 3.2 and extract the watermark using Eq. (3.4). Finally, the
scrambled watermark is rearranged to its original sequence using the pre-defined
49
key.
Ŵp (x, y) =
Hk3 (x,y)−HWk3 (x,y)
α(r)
k = {1, 2, 3, ..., 16}
p = {1, 2, 3, ..., 16}
(3.4)
r = {1, 2, 3, ..., 16}
Performance Improvement using GA
The watermarked image is subjected to various watermarking attacks which degrade the performance of watermarking methods such as quality and robustness
which must be maximized for a watermarking method, but these parameters are
inversely related with each other i.e. if quality increases, robustness suffers and
vice-versa. In this method, optimum values of these parameters are dependent on
the suitable values of strength factor (α). Therefore, this method also uses GA
optimization method for selecting the values of α which optimizes the quality and
robustness in terms of fitness function as described in 2.3.
3.4
Experimental Results
To compare the performance of proposed and considered image watermarking
methods, two popularly used 24-bit RGB color images namely, lena and mandrill
each of size 512 × 512 are used as host images and two gray scale images namely,
RTU logo and aeroplane image each of size 64 × 64 are used as watermark images
to embed them in the host images and are shown in Figure 3.3. All the considered
images have been taken from USC-SIPI image database [112] except RTU logo
which is taken from Rajasthan Technical University, Kota, India.
(a) Lena
(b) Mandrill
(c) RTU logo
(d) Aeroplane
Figure 3.3: (a)-(b). RGB host images and (c)-(d). Gray scale watermark images.
The performance of the proposed method is compared with method proposed
in Chapter 2 in which DWT and GA have been used to increases the quality
and robustness of image watermarking method. To compare the objective test
50
for quality measures of resultant watermarked images using proposed method and
considered methods, CPSNR [42] performance parameters are calculated and depicted in Table 3.1. From Table 3.1, it is validated that all the methods maintain
the quality of the watermarked image in terms of CPSNR (> 35dB). However,
the proposed method shows better results in terms of CPSNR as compared to
proposed DWT-based method.
Table 3.1: Comparison of CPSNR values of watermarked images resultant from
proposed and considered methods.
Used
Watermark
Watermarked
images
Proposed
DWT-based
method
[Chapter 2]
Proposed SPTbased Method
1.
RTU Logo
Lena
35.92
37.23
2.
RTU Logo
Mandrill
35.67
36.87
3.
Aeroplane
Lena
35.61
37.11
4.
Aeroplane
Mandrill
35.59
36.08
S.No.
The robustness of the proposed methods has been tested by applying different
attacks on the watermarked images. This chapter consists of filtering (mean,
median, wiener), noise (gaussian, poisson, salt and pepper), JPEG compression,
rotation, scaling, and cropping attacks. The attacks are applied on all the four
watermarked images embedded with RTU logo and aeroplane watermark images.
The comparison of NC values after applying the attacks on watermarked images
embedded with RTU logo and aeroplane image are depicted in Table 3.2. From
Table 3.2, it is observed that the robustness of watermarked images embedded
with RTU logo have higher values of NC as compared to aeroplane image due to
the coarseness of aeroplane image as compared to RTU logo. Moreover, it can be
concluded from Figure 3.4 and Figure 3.5 which shows the extracted RTU logo
and Aeroplane watermarks by proposed method respectively, for all the considered
attacks have been depicted. Therefore, it is validated from the results that the
proposed method using SPT produces high quality and better robust watermarked
images and can be utilized for content authentication to protect the copyrighted
images. The comparative results show that the proposed method outperforms
other methods for all the considered attacks.
3.5
Results and Discussions
This chapter introduces a SPT-based image watermarking method using UCS and
GA. The uncorrelated color space increases the effective utilization of all color
51
52
0.76
0.92
0.99
Median filtering (3 × 3)
Wiener filtering (3 × 3)
Mean filtering (3 × 3)
Gaussian noise (0.006)
1
2
3
4
0.88
0.96
Salt and pepper noise
JPEG compression (10%)
JPEG compression (90%)
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
6
7
8
9
10
11
12
13
14
15
16
0.62
0.74
0.84
0.96
0.99
0.98
0.98
0.71
0.96
Poisson noise
5
0.88
0.87
Proposed
DWTbased
method
[Chapter 2]
Attacks
S.No.
0.63
0.79
0.90
0.97
0.98
0.98
0.98
0.97
0.88
0.72
0.97
0.94
0.91
0.98
0.93
0.79
Proposed
SPT-based
Method
lena
0.61
0.73
0.83
0.94
0.97
0.97
0.96
0.95
0.86
0.70
0.94
0.86
0.85
0.97
0.90
0.75
0.62
0.73
0.89
0.95
0.98
0.98
0.98
0.96
0.88
0.71
0.96
0.90
0.86
0.98
0.90
0.80
Proposed
SPT-based
Method
Mandrill
Proposed
DWTbased
method
[Chapter 2]
RTU Logo
0.61
0.73
0.84
0.95
0.98
0.98
0.97
0.95
0.87
0.70
0.95
0.87
0.86
0.98
0.91
0.75
Proposed
DWTbased
method
[Chapter 2]
0.62
0.73
0.86
0.96
0.98
0.98
0.98
0.97
0.90
0.71
0.96
0.88
0.87
0.98
0.92
0.78
Proposed
SPT-based
Method
lena
0.60
0.72
0.82
0.93
0.96
0.96
0.95
0.93
0.85
0.69
0.93
0.85
0.84
0.96
0.89
0.74
0.61
0.73
0.83
0.94
0.97
0.97
0.96
0.94
0.86
0.70
0.94
0.90
0.87
0.97
0.91
0.74
Proposed
SPT-based
Method
Mandrill
Proposed
DWTbased
method
[Chapter 2]
Aeroplane Image
Table 3.2: Comparison of robustness in terms of NC values obtained after applying attacks on the watermarked images.
Extracted RTU logo Watermark Image After Attacks
Median (3x3)
Wiener (3x3)
Mean (3x3)
Gaussian (0.006)
JPEG (10%)
JPEG (90%)
Poisson
Salt & Pepper
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
Figure 3.4: Extracted RTU logo watermarks by proposed method after applying
considered attacks.
53
Extracted Aeroplane Watermark Image After Attacks
Median (3x3)
Wiener (3x3)
Mean (3x3)
Gaussian (0.006)
JPEG (10%)
JPEG (90%)
Poisson
Salt & Pepper
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
Figure 3.5: Extracted Aeroplane image watermarks by proposed method after
applying considered attacks.
54
channels of host image as compared to correlated color spaces while GA optimizes
the strength factors and improves the quality and robustness of the proposed
method. The results validates that the proposed method using SPT and UCS
is better than existing methods which are based on DWT for all the considered
performance parameters. Hence, it is concluded that SPT-based method using
UCS outperforms as compared with the DWT.
The next two chapters describe the watermarking methods for embedding the
color watermark images into color host images.
55
Chapter 4
Digital Image Watermarking using Discrete Wavelet Transform on Color Watermark Images
4.1
Introduction
Previous two chapters (2 and 3) present the image watermarking method for the
protection of color host images by embedding gray-scale images using the DWT
and SPT, respectively. Further, the capabilities of GA has been utilized in both
the presented methods to enhance the performance by optimally selecting the
strength factors as discussed in the previous chapters. Finally, it is validates by
the analysis of experimental results that out of both the proposed methods, the
SPT-based method using UCS outperforms among the class. Furthermore, all
the researchers are trying to develop a color image watermarking by embedding
the color watermark images into the color host images, since all the images are
available in the color format. Hence, this chapter introduces a novel DWT-based
color image watermarking method for protection of color images by embedding
the color watermark. Further, this method uses the powerfulness of ABC method
for optimizing the various embedding parameters.
DWT-based image watermarking methods are capable to embed a fairly good
quality of watermark and it can recover the watermark from watermarked image
effectively. The quality and robustness of DWT-based methods depend on the
selection of particular filter bank and decomposition level. Agreste and Andaloro
[58] developed a DWT-based method for any size of image which implants watermark data into high-frequency sub-bands of DWT coefficient. This watermarked
data is imperceptible as per HVS directions. Later, Agreste and Andaloro [59]
modified the previous method by changing the filer bank by Daubechies-2 and ob-
served that it is more robust to geometric, filtering, and StirMark attacks with a
low rate of false alarm. Ghouti et al. [41] selected balanced multi-wavelets for the
data hiding and found that the method is more robust against the standard watermarking attacks. Vahedi et al. [42] exploited the advantage of symlet-4 filter bank
to increase the quality and robustness of watermarking method as compared to
existing methods. For embedding the watermark, DWT-based methods use three
or higher level decompositions [39, 40, 42]. Vahedi et al. [42] showed that three
level decomposition with all the four sub-spaces namely; approximation, horizontal, vertical, and diagonal along with symlet-4 filter bank provide better results for
image watermarking. Therefore, in this method, DWT has been used with three
level of decomposition and symlet-4 filter bank for embedding the watermark.
Moreover, to increase the quality and robustness of watermarking methods,
researchers also used many optimization methods like GA, PSO, ABC, differential
evolution (DE), etc. [42, 55, 78, 81, 82]. PSO and GA have been used by Vahedi
et al. [42, 80] to calculate the optimized strength of watermark which improves
the robustness of existing methods. Therefore, in this chapter, the strength factor
of the proposed method has been optimized using three optimization function
namely; GA, ABC, and DE and their performance are compared.
Initially, image watermarking methods hide the watermark message into gray
scale or color host image in the form of bits or bit stream, and later these digital
bit streams are replaced by some pictorial shape like image [39, 40]. Generally,
various watermarking methods embed the gray scale or binary image watermark
while very few work has been reported for color watermark [96, 97, 98], though
most multimedia images are available in color. Generally, the researchers used
RGB, Y Cb Cr , YIQ, HSI, HSV, etc. color space models for both watermarks and
host images in their digital watermarking method. Golea et al. [97] proposed
the SVD-based RGB color image watermarking for embedding the color RGB
watermark into the RGB host image. Recently, Su et al. [98] presented QR
decomposition method for embedding the RGB color watermark into RGB host
image. In his paper, they concluded that the proposed method is robust against
the attacks such as compression, filtering, cropping, etc. The above mentioned
color models are correlated i.e. the image components are not independent and
change in one component may affect the other components of the image. This
imposes the constraints for the researchers to use only one color component at a
time for embedding the watermark data. However, there exist some uncorrelated
color models such as Lab, Lαβ, etc. [43, 102, 104] which may be used in color image
watermarking to increase the robustness and quality by using all the color image
components of host and watermark images. Chou and Wu [96] embedded the color
58
watermark in Lab color space using less computationally complex spatial-domain
color image watermarking method. However, the robustness of this method is
poor.
Therefore, due to the limitations of correlated color spaces, rare use of colored
watermark images, and powerfulness of DWT, this chapter proposes a novel DWTbased color image watermarking method using UCS. Further, ABC has been used
to optimize the strength factor of the proposed watermarking method.
Rest of the chapter is organized as follows. Section 4.2 and 4.3 describes the
working of ABC and DE methods respectively, used in this method. GA, DWT,
and UCS are already been described in Section 2.2 of Chapter 2. The proposed
method is presented in Section 4.4. Section 4.5 describes the method validation
parameters and experimental results are discussed in Section 4.6. Finally, Section
4.7 concludes the chapter.
4.2
Artificial Bee Colony (ABC) Method
ABC [82] is a newly developed swarm intelligence-based method which is inspired
by the intelligent food foraging behavior of honey bees. Similar to the other
population-based methods, ABC solution search process is an iterative process.
After, initialization of the ABC parameters and swarm, it requires the repetitive
iteration of the three phases namely; employed bee phase, onlooker bee phase,
and scout bee phase. The initialization of the swarm and details of each phase are
described in the following sections.
Initialization of the Swarm
The parameters for ABC are the number of food sources, number trials after which
a food source is considered to be abandoned, and termination criteria. In the basic
ABC, the number of food sources is equal to the employed bees or onlooker bees.
Initially, a uniformly distributed initial swarm of SN food sources, where each
food source xi (i = 1, 2, ..., SN) is a D-dimensional vector, are generated. Here
D is the number of variables in the optimization problem and xi represent the
ith food source in the swarm. Each food source is generated as mentioned in Eq.
(4.1) [82].
xij = xminj + rand[0, 1](xmaxj − xminj )
(4.1)
here, xminj and xmaxj are bound of xi in j th direction and rand[0, 1] is a uniformly
distributed random number in the range [0, 1].
59
Employed Bee Phase
In employed bee phase, employed bees modify the current solution (food source)
based on the information of individual experience and the fitness value of the new
solution. If the fitness value of the new solution is higher than that of the old
solution, the bee updates her position with the new one and discards the old one.
The position update equation for ith candidate in this phase is presented in Eq.
(4.2) [82].
vij = xij + φij (xij − xkj )
(4.2)
here, k ∈ {1, 2, ..., SN} and j ∈ {1, 2, ..., D} are randomly chosen indices. k must
be different from i. φij is a random number between [−1, 1].
Onlooker Bees Phase
After completion of the employed bees phase, the onlooker bees phase starts.
In onlooker bees phase, all the employed bees share the new fitness information
(nectar) of the new solutions (food sources) and their position information with
the onlooker bees in the hive. Onlooker bees analyze the available information and
select a solution with a probability, probi , related to its fitness. The probability
probi may be calculated using Eq. (4.3) [82].
f itnessi
probi = SN
i=1 f itnessi
(4.3)
here, f itnessi is the fitness value of the solution i. As in the case of the employed
bee, it produces a modification on the position in its memory and checks the fitness
of the candidate source. If the fitness is higher than that of the previous one, the
bee memorizes the new position and forgets the old one.
Scout Bees Phase
If the position of a food source is not updated up to predetermined number of
cycles, then the food source is assumed to be abandoned and scout bees phase
starts. In this phase, the bee associated with the abandoned food source becomes
scout bee and the food source is replaced by a randomly chosen food source within
the search space. In ABC, predetermined number of cycles is a crucial control
parameter which is called limit for abandonment. Assume that the abandoned
source is xi . The scout bee replaces this food source by a randomly chosen food
60
source which is generated as given in Eq. (4.4) [82].
xji = xjmin + rand[0, 1](xjmax − xjmin ),
f or
j ∈ {1, 2, ..., D}
(4.4)
here, xminj and xmaxj are bound of xi in j th direction. The pseudo-code of the
ABC is shown in Algorithm 4.1 [82]. In this method, ABC is being used to
optimize the watermarking parameters for increasing the quality and robustness
of the proposed method.
Algorithm 4.1 Artificial Bee Colony Algorithm
Initialize the parameters;
while Termination criteria is not satisfied do
1. Employed bee phase for generating new food sources.
2. Onlooker bees phase for updating the food sources depending on their nectar
amounts.
3. Scout bee phase for discovering the new food sources in place of abandoned
food sources.
4. Memorize the best food source found so far.
5. If a termination criteria is not satisfied, go to step 1; otherwise output the best
solution found so far.
end while
4.3
Differential Evolution (DE) Algorithm
DE algorithm is relatively a simple, fast, and population-based stochastic search
method [114] which falls under the category of Evolutionary Algorithms (EAs).
However, it differs significantly from EAs, e.g. in EAs, crossover is applied first
to generate a trial vector, which is then used within the mutation operation to
produce one offspring while, in DE, crossover follows the mutation operator [115].
DE has several methods of selecting the target vector, number of difference vectors, and the type of crossover [114]. This paper uses DE/rand/1/bin scheme in
which DE stands for differential evolution, ‘rand’ specifies that the target vector
is selected randomly, ‘1’ is for number of differential vectors, and ‘bin’ notation is
for binomial crossover. The popularity of DE is due to its applicability to a wider
class of problems and ease of implementation. The detailed description of DE is
as follows:
Like other population-based search methods, DE also searches the solution
using a population of potential solutions (individuals). In a D-dimensional search
space, an individual is represented by a D-dimensional vector (xi1 , xi2 , ..., xiD )
where i = 1, 2, ..., NP and NP is the population size (number of individuals).
61
In DE, there are three operators: mutation, crossover, and selection. Initially, a
population is generated randomly with uniform distribution followed by mutation,
crossover, and selection operators to generate a new population. Offspring vector
generation is a crucial step in DE process. The two operators (mutation and
crossover) are used to generate the offspring vectors. The selection operator is
used to select the best vector between offspring and parent for the next generation.
DE operators are explained briefly in the following sections.
Mutation
A trial vector is generated by the DE mutation operator for each individual of
the current population. For generating the trial vector, a target vector is mutated
with a weighted differential. An offspring is produced in the crossover operation
using the newly generated trial vector. If G is the index for generation counter,
the mutation operator for generating a trial vector ui (G) from the parent vector
xi (G) is defined as follows:
• Select a target vector, xi1 (G), from the population, such that i = i1 .
• Again, randomly select two individuals, xi2 and xi3 , from the population
such that i = i1 = i2 = i3 .
• Then the target vector is mutated for calculating the trial vector as follows:
Variation Component
ui (G) = xi1 (G) + F × (xi2 (G) − xi3 (G))
Step size
(4.5)
here F ∈ [0, 1] is the mutation scale factor which is used for controlling the
amplification of the differential variation [115].
Crossover
Offspring xi (G) is generated using the crossover of parent vector (xi (G)) and the
trial vector (ui (G)) as follows:
⎧
⎨u (G), if j ∈ J
ij
xij (G) =
⎩x (G), otherwise.
ij
(4.6)
here J is the set of crossover points or the points that will go under perturbation
and xij (G) is the j th element of the vector xi (G).
62
Different methods may be used to determine the set J of crossover points in
which binomial crossover and exponential crossover are the most frequently used
[115]. In this chapter, the DE and its variants are implemented using binomial
crossover whereas for a D dimensional problem, the crossover points are randomly
selected from the set of possible points, {1, 2, . . . , D}. Algorithm 4.2 shows the
steps of binomial crossover to generate crossover points [115].
Algorithm 4.2 Binomial Crossover
Let CR represents the probability with which the considered crossover points will be included.
U (1, D) is a uniformly distributed random integer between 1 and D.
J =φ
j ∗ ∼ U (1, D);
J ← J ∪ j∗;
for each j ∈ 1...D do
if U (0, 1) < CR and j = j ∗ then
J ← J ∪ j;
end if
end for
Selection
There are two functions for the selection operator: First, it selects the individual
for the mutation operation to generate the trial vector and second, it selects the
best between the parent and the offspring based on their fitness value for the next
generation. If fitness of the parent is greater than the offspring then parent is
selected otherwise offspring is selected:
⎧
⎨x (G), if f (x (G)) > f (x (G)).
i
i
i
xi (G + 1) =
⎩x (G), otherwise.
i
(4.7)
This ensures that the population’s average fitness does not deteriorate. The
Pseudo-code for DE method is described in Algorithm 4.3 [115].
Algorithm 4.3 Differential Evolutionary Algorithm
Let F and CR are the control parameters termed as scale factor and crossover probability respectively.
Let P is the population vector.
Initialize the control parameters F and CR;
Create and initialize the population P (0) of N P individuals;
while termination condition do
for each individual xi (G) ∈ P (G) do
Evaluate the fitness f (xi (G));
Create the trial vector ui (G) by applying the mutation operator;
Create an offspring xi (G) by applying the crossover operator;
if f (xi (G)) is better than f (xi (G)) then
Add xi (G) to P (G + 1);
else
Add xi (G) to P (G + 1);
end if
end for
end while
Return the fittest individual as the solution.
63
4.4
Proposed Method
The proposed method explores the advantages of uncorrelated color space over
the correlated color space to improve the performance of watermarking methods
in terms of quality and robustness. It implants the color watermark image into
the color host image by modifying the decomposed wavelet coefficients of host
image. The each channel of color image is embedded in the corresponding channel
of host image to increase the reliability during the recovery process and protect
against the common signal processing attacks. The proposed method consists
of five phases namely; pre-processing of host and watermark image, watermark
embedding, image post-processing, extraction of watermark, and performance improvement using GA, ABC, and DE. The basic structural design of the proposed
method is shown in Figure 4.1. Since each color image has three channels, the
structural design and methodology represented in Figure 4.1 are repeated for all
the three channels. The details of each phase of the proposed method is described
in the following sections.
Image Pre-processing
In image pre-processing, both the host RGB color image (H) and watermark image
(W ) are transformed into UCS color space using Eq. (2.8) which produces six
independent image components (three for host and three for watermark) namely;
HU , HC , and HS for host image and WU , WC , and WS for watermark image.
After transformation, each component of watermark image is divided into 16 nonoverlapping sub-areas as shown in Figure 4.2. These sub-areas of watermark image
are then re-arranged or scrambled by some pre-defined sequence or key to introduce
one more level of security and enforced the user to use the key for the extraction
of the image from watermarked image.
Further, third level of decomposition is applied on each component of host image using symlet-4 wavelet function. The resultant third level wavelet coefficients,
(Hk3 , k = {LL, HL, LH, HH}) is chosen for embedding process.
Watermark Embedding
After pre-processing of the images, the scrambled watermark is embedded into
the DWT coefficients of host image. The third level decomposition coefficients of
host image are HLL3 (x, y), HHL3 (x, y), HLH3 (x, y), and HHH3 (x, y) representing
the approximation, horizontal, vertical, and diagonal details of 3-DWT decomposed host image respectively. In order to increase the reliability and robustness
64
Figure 4.1: Structural design of proposed method.
Figure 4.2: Watermark division.
65
of proposed method against the malicious attacks, it is desired to hide each color
component of the watermark image in corresponding component of host coefficients. Therefore, approximation details of the third level coefficients are divided
into 16 non-overlapping areas as depicted in Figure 4.3.
Figure 4.3: Partitioning of 3-DWT coefficients.
Now, the scrambled watermark is embedded into approximation details of the
third level decomposed host image coefficients by using Eq. (4.8).
HWk3 (x, y) = Hk3 (x, y) + α(r)Wp (x, y)
k = {LL, HL, LH, HH}
p = {1, 2, 3, ..., 16}
(4.8)
r = {1, 2, 3, ..., 16}
here, k represents the sub-areas of host image, p is the intended block of watermark, and α denotes the strength of modification done in the host coefficients.
Post-processing
After embedding all the color channels of UCS watermark image into corresponding DWT host image coefficients, inverse DWT (IDWT) is taken for watermarked
host image coefficients which returns watermarked image in UCS color space. Finally, UCS watermarked image is reconverted into RGB color space.
Watermark Extraction
The watermark extraction process requires watermarked image and pre-defined
key. To extract the watermark, RGB watermarked image is transformed into UCS
color space and then 3-DWT decomposition using symlet-4 wavelet filter bank on
66
each channel is applied. To gather the fraction watermarked image, a reverse
process of embedding ia applied as shown in Figure 4.1 and using Eq. (4.9).
Finally, the scrambled watermark image is rearranged to its original sequence.
Ŵp (x, y) =
Hk3 (x,y)−HWk3 (x,y)
α(r)
k = {LL, HL, LH, HH}
p = {1, 2, 3, ..., 16}
r = {1, 2, 3, ..., 16}
(4.9)
Performance Improvement using Optimization Methods
The watermarked image HWk3 is subjected to various watermarking attacks which
degrade the performance of watermarking methods. The design of the apposite
attacks are as important as to design a method for protection of multimedia contents because they require to test the robustness and security of the newly design
methods for protection of multimedia contents. There are mainly two parameters namely quality and robustness which must be maximized for a watermarking
method, but these parameters are inversely related with each other i.e. if quality increases, robustness suffers and vice-versa. In this method, optimum values
of these parameters are dependent on the suitable values of strength factor (α).
Therefore, this method uses three optimization methods namely; GA, ABC, and
DE for selecting the values of α which optimizes the quality and robustness in
terms of fitness function. The results of all the considered optimization methods
have been compared and analyzed.
There are 16 strength factors used in this chapter which become the dimension
of each individual in ABC/DE/GA method having the population size 50. The
optimized values of these 16 strength factors are calculated by minimizing the
following fitness function which is the modified version of Vahedi et al. [42],
90%
100
100
+
+2×
F itness =
[1 − NCjpg(Q) ]
CP SNR SSIM
Q=60%
+
50%
[1 − NCjpg(Q)] +
+
[1 − NCscale(i) ] +
i=1
+
4
[1 − NCf ilter(i) ]
i=1
Q=10%
3
5
3
[1 − NCnoise(i) ]
i=1
[1 − NCcrop(i) ] + [1 − NCrotation ]
i=1
67
(4.10)
here, CPSNR is composite peak signal-to-noise ratio, SSIM is structural similarity,
and NC is is normalized correlation calculated for the attacks namely; JPEG compression (10 to 90%), filtering (wiener, mean, median), scaling (0.6 to 1.2 factor),
noise addition (poisson, salt and pepper, gaussian), rotation (10 ), and cropping
(10 to 40 %). These values are calculated by applying the mentioned attacks on
watermarked image. The setting of the parameters of three optimization methods
depicts in Table 4.1 for this experiment are given:
Table 4.1: Setting of the parameters of three optimization methods namely; GA,
ABC, and DE.
GA
ABC
DE
Population size = 50.
Colony size N P = 50.
Population size N P = 50.
Crossover rate = 0.6.
φij = rand[−1, 1].
The scale factor which controls
the implication of the differential
variation F = 0.5.
Crossover type = typically two
point.
Number of food sources SN =
N P/2.
The crossover probability CR =
0.9.
Number of generations = 1000.
limit = 1000.
limit = 1000.
Mutation types = bit flip.
The stopping criteria is maximum number of function evaluations (which is set to be 500) is
reached.
The stopping criteria is set to the
maximum number of iterations
which is 500 for this experiment.
4.5
Method Validation
The results obtained from the proposed method are compared with the methods
of Chou and Wu [96] and Su et al. [98], who also embedded the color watermark
images into the color host images. To compare these methods, quality and robustness parameters are considered. The quality of watermarked image can be assess
by two ways namely; subjective and objective tests. Subjective test is done by
10 humans beings on the scale of 0 (very poor) to 5 (excellent) while objective
test calculates the parameters namely; CPSNR and SSIM. CPSNR measures the
degree of similarity between the original and watermarked image and is calculated
as follows [42]:
CP SNR =
10 M × N × 2552
log M N
2
3 s
m=1
n=1 [H(m, n, s) − HW (m, n, s)]
(4.11)
SSIM also measures the degree of similarity by including the three aspects of HVS
namely; loss of correlation (c), luminance distortion (l ), and contrast distortion
68
(s). It is formulated as follows [98]:
SSIM = l(H, HW )c(H, HW )s(H, HW )
where,
(4.12)
l(H, HW ) =
2μH μHW + C1
μ2H + μ2HW + C1
(4.13)
c(H, HW ) =
2σH σHW + C2
2
2
σH
+ σH
+ C2
W
(4.14)
σHHW + C3
σH σHW + C3
(4.15)
s(H, HW ) =
here μ and σ are mean and standard deviation respectively while C1 , C2 , and
C3 are three positive constants used to avoid a null denominator. The values of
CPSNR and SSIM must be maximized for effective quality of watermarked image.
The robustness of watermarking method shows the degree of similarity between
the original watermark image and extracted watermark image after applying the
attacks and is measured by normalized correlation (NC) which is formulated as
shown in Eq.4.16:
1
NC =
3 s
L
m=1
L
[W (m, n, s) × Ŵ (m, n, s)]
n=1
L
L
2
m=1
n=1 W (m, n, s)
(4.16)
The values of NC lie in the rage of 0 (no similarity) to 1 (similar) and are calculated
for the extracted watermarks without attack and with attacks.
4.6
Experimental Results
To compare the performance of proposed and considered color image watermarking
methods, four popularly used 24-bit RGB color images namely; lena, mandrill,
pepper, and sailboat, each of size 512 × 512, are used as host images which are
shown in Figure 4.4. In this chapter, one RGB color logo (64 × 64) and one RGB
color image (64 × 64) namely; RTU logo and aeroplane color image respectively,
are used as watermark images which are shown in Figure 4.5. All the considered
images have been taken from USC-SIPI image database [112] except RTU logo
which is taken from Rajasthan Technical University, Kota, India.
69
(a) Lena
(b) Mandrill
(c) Pepper
(d) Sailboat
Figure 4.4: RGB host images.
(a) RTU logo
(b) Aeroplane
Figure 4.5: RGB watermark images.
In the pre-processing step of the proposed method, each host and watermark
images are converted into UCS color space followed by third level decomposition of
host images and scrambling of watermark images. Figure 4.6 shows the converted
UCS images of host and watermark and third level decomposed lena host image
is represented in Figure 4.7. The watermark image goes through the process of
scrambling. One of the possible scrambled watermark images of RTU logo and
aeroplane are shown in Figure 4.8.
Figure 4.7: Three level decomposed host lena image using DWT.
70
(a)
(b)
(c)
(d)
(e)
(f)
Figure 4.6: UCS host and watermark images (a). Lena, (b). Mandrill, (c).
Pepper, (d). Sailboat, (e). RTU Logo, and (f). Aeroplane.
(a) RTU logo
(b) Aeroplane
Figure 4.8: Representative scrambled UCS watermark images.
Now, the watermark images are embedded into each of the considered host
images and the resultant watermarked images are shown in Figure 4.9.
To compare the objective test for quality measures of resultant watermarked
images using proposed and considered methods, CPSNR and SSIM performance
parameters are calculated and shown in Table 4.2. From Table 4.2, it is validated
that all the methods including proposed method maintain the quality of the watermarked image in terms of CPSNR (> 35dB) and SSIM (> 0.96). However,
the proposed method shows lower values of CPSNR and SSIM as compared to
other considered methods due to the hiding of complete watermark into all three
channels of host image while existing methods hide in only one channel of host
image. Moreover, subjective test on the resultant watermarked images is performed and presented in Table 4.3. The results of subjective test show that the
proposed method effectively embeds the watermarks which is imperceptible by
human beings.
71
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figure 4.9: RGB watermarked images embedded by using DE-based proposed
method (a)-(d) RTU logo and (e)-(h) Aeroplane image
Table 4.2: Comparison of CPSNR and SSIM values of watermarked images
resultant from proposed and considered methods.
Quality
Watermarked Su et al.
Parameters images
[98]
Chou
and Wu
[96]
Proposed
Method
using
GA
Proposed
Method
using
ABC
Proposed
Method
using
DE
1.
CPSNR
2.
SSIM
37.79
37.71
37.01
37.32
0.98
0.98
0.97
0.97
35.81
35.13
35.07
35.00
0.98
0.97
0.96
0.97
35.92
35.67
35.23
35.06
0.98
0.96
0.96
0.97
36.01
35.89
35.41
35.21
0.98
0.97
0.96
0.97
S.No.
Lena
Mandrill
Pepper
Sailboat
Lena
Mandrill
Pepper
Sailboat
36.57
36.42
36.61
36.52
0.98
0.98
0.96
0.98
Table 4.3: Average subjective quality comparison of original and watermarked
images by 10 human beings in the scale of 0 to 5.
Watermark
image
Watermarked Su et al.
image
[98]
1.
RTU Logo
2.
Aeroplane
Lena
Mandrill
Pepper
Sailboat
Lena
Mandrill
Pepper
Sailboat
S.No.
5
5
5
5
5
5
5
5
Average Score of 10 Human beings
Proposed Proposed
Chou
Method
Method
and Wu
using
using
[96]
ABC
GA
5
5
5
5
5
5
5
5
72
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Proposed
Method
using
DE
5
5
5
5
5
5
5
5
To show the effectiveness of the proposed watermarking method, the extracted
watermarks from the watermarked images and their NC values are shown in Figure
4.10 for each of the considered method. From Figure 4.10, it is visualized that the
proposed method and Su et al. [98] have highest NC values (1.0) for all extracted
watermarks and hence outperforms the method of Chou and Wu [96]. The columns
of Figure 4.10 show extracted watermarks from considered watermarked images
and first five rows show the extraction of RTU logo while last five show extraction
of aeroplane image using the proposed and considered methods of watermarking.
The robustness of the proposed method has been tested by applying different
attacks on the watermarked images. In this chapter, attacks have been categorized into two classes namely; common signal processing attacks and geometric
attacks. The considered common signal processing attacks consists of filtering attacks (mean, median, wiener), noise attacks (gaussian, poisson, salt and pepper),
and JPEG compression attacks while rotation, scaling, and cropping are the considered geometric attacks. The attacks are applied on all the eight watermarked
images embedded with RTU logo and aeroplane images. The comparison of NC
values after applying the common signal processing attacks on watermarked images embedded with RTU logo are depicted in Table 4.4 while Table 4.5 shows
the NC values for watermarked images embedded with aeroplane image. After
applying the geometric attacks, the measured NC values for both the watermark
images are compared in Table 4.6. From Tables 4.4, 4.5, and 4.6, it is observed
that the robustness of watermarked images embedded with RTU logo have higher
values of NC as compared to aeroplane image due to the coarseness of aeroplane
image. The comparative results show that the proposed method using DE outperforms other methods for all the considered attacks except JPEG compression
and rotation where the method of Su et al. shows slightly better robustness. The
similar performance of the proposed method can be observed from Figure 4.11 and
Figure 4.12 where the extracted RTU logo and Aeroplane watermarks by proposed
method using DE respectively, for all the considered attacks have been depicted.
Therefore, it is validated from the results that the proposed method using DE
produces high quality and better robust watermarked images and can be utilized
for content authentication to protect the copyrighted images.
4.7
Results and Discussions
This chapter proposes a novel DWT-based color image watermarking method using UCS. The use of uncorrelated color space increases the effective utilization of
all color channels of host image which is not feasible in correlated color spaces.
73
Used Watermarked Image
Method
a. Lena
b. Mandrill
c. Pepper
d. Sailboat
1.00
0.99
0.99
0.98
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
0.99
0.98
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Chou and Wu [96]
(NC)
Su et al. [98]
(NC)
Proposed Method using GA
(NC)
Proposed Method using ABC
(NC)
Proposed Method using DE
(NC)
Chou and Wu [96]
(NC)
Su et al. [98]
(NC)
Proposed Method using GA
(NC)
Proposed Method using ABC
(NC)
Proposed Method using DE
(NC)
Figure 4.10: Comparison of extracted watermarks by considered and proposed
methods along with their corresponding NC values. Columns shows extracted
watermarks from watermarked image namely (a). Lena, (b). Mandrill, (c).
Pepper, and (d). Sailboat using the considered and proposed methods mentioned
in first column. First five rows shows the extraction of RTU logo while last five
shows extraction of aeroplane
watermark image.
74
75
Gaussian
(0.006)
Poisson noise
Salt and
noise
JPEG compression
(10%)
JPEG compression
(30%)
JPEG compression
(60%)
JPEG compression
(90%)
6
7
8
9
10
11
12
pepper
0.44
0.34
0.21
0.14
0.90
0.86
0.90
0.41
Mean filtering (5 ×
5)
5
noise
0.54
Mean filtering (3 ×
3)
0.11
4
Wiener
(3 × 3)
3
filtering
0.18
0.67
Median
(5 × 5)
2
filtering
filtering
Median
(3 × 3)
Attacks
1
S.No.
Chou
and
Wu
[96]
0.93
0.90
0.88
0.71
0.91
0.83
0.82
0.87
0.96
0.87
0.63
0.73
Su
et
al.
[98]
0.92
0.83
0.87
0.70
0.89
0.82
0.81
0.86
0.95
0.86
0.62
0.72
GA
0.90
0.87
0.85
0.69
0.93
0.85
0.84
0.89
0.98
0.89
0.64
0.74
0.91
0.88
0.86
0.70
0.94
0.86
0.84
0.90
0.99
0.90
0.65
0.75
ABC DE
Using
Proposed Method
lena
0.41
0.32
0.20
0.13
0.84
0.80
0.84
0.38
0.50
0.62
0.10
0.17
Chou
and
Wu
[96]
0.86
0.84
0.82
0.66
0.85
0.77
0.76
0.81
0.89
0.81
0.59
0.68
Su
et
al.
[98]
0.86
0.83
0.81
0.65
0.84
0.76
0.75
0.80
0.88
0.80
0.58
0.67
GA
0.84
0.81
0.79
0.64
0.86
0.79
0.78
0.83
0.91
0.83
0.60
0.69
0.85
0.82
0.80
0.65
0.87
0.80
0.79
0.83
0.92
0.83
0.60
0.70
ABC DE
Using
Proposed Method
Mandril
0.43
0.33
0.21
0.14
0.88
0.84
0.88
0.40
0.53
0.66
0.11
0.18
Chou
and
Wu
[96]
0.91
0.88
0.86
0.70
0.89
0.81
0.80
0.85
0.94
0.85
0.62
0.72
Su
et
al.
[98]
0.90
0.87
0.85
0.69
0.88
0.81
0.80
0.84
0.93
0.84
0.61
0.71
GA
0.88
0.86
0.84
0.67
0.91
0.83
0.82
0.87
0.96
0.87
0.63
0.73
0.89
0.86
0.84
0.68
0.92
0.84
0.83
0.88
0.97
0.88
0.64
0.74
ABC DE
Using
Proposed Method
Pepper
0.42
0.33
0.20
0.13
0.86
0.83
0.86
0.39
0.52
0.64
0.11
0.17
Chou
and
Wu
[96]
0.89
0.86
0.84
0.68
0.87
0.80
0.79
0.84
0.92
0.84
0.60
0.70
Su
et
al.
[98]
0.88
0.86
0.84
0.67
0.86
0.79
0.78
0.83
0.91
0.83
0.60
0.69
GA
0.87
0.84
0.82
0.66
0.89
0.81
0.80
0.85
0.94
0.85
0.62
0.71
0.87
0.85
0.83
0.67
0.90
0.82
0.81
0.86
0.95
0.86
0.62
0.72
ABC DE
Using
Proposed Method
Sailboat
Table 4.4: Comparison of robustness in terms of NC values obtained after applying common signal processing attacks on the
watermarked image embedded with RTU logo
76
Gaussian
(0.006)
Poisson noise
Salt and
noise
JPEG compression
(10%)
JPEG compression
(30%)
JPEG compression
(60%)
JPEG compression
(90%)
6
7
8
9
10
11
12
pepper
0.4
0.27
0.16
0.11
0.82
0.7
0.81
0.3
Mean filtering (5 ×
5)
5
noise
0.42
Mean filtering (3 ×
3)
0.06
4
Wiener
(3 × 3)
3
filtering
0.13
0.51
Median
(5 × 5)
2
filtering
filtering
Median
(3 × 3)
Attacks
1
S.No.
Chou
and
Wu
[96]
0.91
0.89
0.86
0.69
0.84
0.81
0.83
0.87
0.95
0.83
0.62
0.71
Su
et
al.
[98]
0.90
0.88
0.85
0.68
0.83
0.80
0.82
0.86
0.94
0.82
0.61
0.70
GA
0.88
0.86
0.83
0.67
0.86
0.83
0.85
0.89
0.97
0.85
0.63
0.72
0.89
0.87
0.84
0.68
0.87
0.83
0.86
0.90
0.98
0.86
0.64
0.73
ABC DE
Using
Proposed Method
lena
0.37
0.25
0.15
0.10
0.76
0.65
0.75
0.28
0.39
0.47
0.06
0.12
Chou
and
Wu
[96]
0.85
0.83
0.80
0.64
0.78
0.75
0.77
0.81
0.88
0.77
0.58
0.66
Su
et
al.
[98]
0.84
0.82
0.79
0.64
0.77
0.75
0.76
0.80
0.87
0.76
0.57
0.65
GA
0.82
0.80
0.78
0.62
0.80
0.77
0.79
0.83
0.90
0.79
0.59
0.67
0.83
0.81
0.78
0.63
0.80
0.78
0.80
0.83
0.91
0.80
0.59
0.68
ABC DE
Using
Proposed Method
Mandril
0.39
0.26
0.16
0.11
0.80
0.69
0.79
0.29
0.41
0.50
0.06
0.13
Chou
and
Wu
[96]
0.89
0.87
0.84
0.68
0.82
0.79
0.81
0.85
0.93
0.81
0.61
0.70
Su
et
al.
[98]
0.88
0.86
0.83
0.67
0.81
0.79
0.81
0.84
0.92
0.81
0.60
0.69
GA
0.87
0.85
0.82
0.66
0.84
0.81
0.83
0.87
0.95
0.83
0.62
0.71
0.87
0.85
0.83
0.66
0.85
0.82
0.84
0.88
0.96
0.84
0.63
0.72
ABC DE
Using
Proposed Method
Pepper
0.38
0.26
0.15
0.11
0.79
0.67
0.78
0.29
0.40
0.49
0.06
0.12
Chou
and
Wu
[96]
0.87
0.85
0.83
0.66
0.81
0.78
0.80
0.84
0.91
0.80
0.60
0.68
Su
et
al.
[98]
0.86
0.85
0.82
0.66
0.80
0.77
0.79
0.83
0.90
0.79
0.59
0.67
GA
0.85
0.83
0.80
0.64
0.82
0.79
0.81
0.85
0.93
0.81
0.61
0.70
0.86
0.84
0.81
0.65
0.83
0.80
0.82
0.86
0.94
0.82
0.61
0.70
ABC DE
Using
Proposed Method
Sailboat
Table 4.5: Comparison of robustness in terms of NC values obtained after applying common signal processing attacks on the
watermarked image embedded with Aeroplane image.
77
0.55
0.73
Cropping (30%)
Cropping (40%)
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
8
1
2
3
4
5
6
7
8
0.43
0.62
0.76
0.86
0.67
0.59
0.5
0.70
0.81
Cropping (20%)
0.91
0.72
0.63
7
Scaling (1.2)
4
6
Scaling (0.9)
3
0.54
0.86
Cropping (10%)
Scaling (0.6)
2
5
Rotation (10 )
Attacks
1
S.No.
Chou
and
Wu
[96]
0.61
0.67
0.79
0.91
0.99
0.97
0.94
0.9
0.60
0.72
0.82
0.93
0.99
0.97
0.95
0.93
Su
et
al.
[98]
0.60
0.66
0.78
0.90
0.98
0.96
0.93
0.89
0.59
0.71
0.81
0.92
0.98
0.96
0.94
0.92
GA
0.62
0.68
0.81
0.93
0.99
0.99
0.96
0.88
0.61
0.73
0.84
0.95
0.99
0.99
0.97
0.91
0.63
0.69
0.81
0.94
1.00
1.00
0.97
0.89
0.62
0.74
0.84
0.96
1.00
1.00
0.98
0.92
ABC DE
Using
Proposed Method
lena
Su
et
al.
[98]
GA
ABC DE
Using
Proposed Method
Mandril
Chou
and
Wu
[96]
0.56
0.67
0.76
0.86
0.92
0.90
0.88
0.86
0.55
0.66
0.75
0.86
0.91
0.89
0.87
0.86
0.57
0.68
0.78
0.88
0.99
0.92
0.90
0.85
0.57
0.69
0.79
0.89
1.00
0.93
0.91
0.86
0.54
0.69
0.79
0.89
0.71
0.62
0.53
0.84
0.40
0.58
0.71
0.80
0.62
0.55
0.47
0.68
0.57
0.62
0.73
0.85
0.92
0.90
0.87
0.84
0.56
0.62
0.73
0.84
0.91
0.89
0.87
0.83
0.58
0.64
0.75
0.86
0.99
0.92
0.89
0.82
0.58
0.64
0.76
0.87
1.00
0.93
0.90
0.83
0.42
0.61
0.74
0.84
0.66
0.58
0.49
0.72
For Aeroplane Watermark Image
0.51
0.65
0.75
0.85
0.67
0.59
0.50
0.80
For RTU Logo Watermark Image
Chou
and
Wu
[96]
0.60
0.66
0.77
0.89
0.97
0.95
0.92
0.88
0.59
0.71
0.80
0.91
0.97
0.95
0.93
0.91
Su
et
al.
[98]
0.59
0.65
0.77
0.88
0.96
0.94
0.91
0.87
0.58
0.70
0.80
0.90
0.96
0.94
0.92
0.90
GA
0.61
0.67
0.79
0.91
0.99
0.97
0.94
0.85
0.60
0.72
0.82
0.93
0.99
0.97
0.95
0.90
0.62
0.68
0.80
0.92
1.00
0.98
0.95
0.86
0.61
0.73
0.83
0.94
1.00
0.98
0.96
0.91
ABC DE
Using
Proposed Method
Pepper
0.41
0.60
0.73
0.83
0.64
0.57
0.48
0.70
0.53
0.67
0.78
0.87
0.69
0.60
0.52
0.83
Chou
and
Wu
[96]
0.59
0.64
0.76
0.87
0.95
0.93
0.90
0.86
0.58
0.69
0.79
0.89
0.95
0.93
0.91
0.89
Su
et
al.
[98]
0.58
0.64
0.75
0.86
0.94
0.92
0.89
0.86
0.57
0.68
0.78
0.88
0.94
0.92
0.90
0.88
GA
0.60
0.66
0.77
0.89
0.99
0.95
0.92
0.83
0.59
0.71
0.80
0.91
0.99
0.95
0.93
0.87
0.60
0.66
0.78
0.90
1.00
0.96
0.93
0.84
0.59
0.71
0.81
0.92
1.00
0.96
0.94
0.88
ABC DE
Using
Proposed Method
Sailboat
Table 4.6: Comparison of robustness in terms of NC values obtained after applying geometric attacks on the watermarked images.
Extracted RTU logo Watermark Image After Attacks
Median (3x3)
Median (5x5)
Wiener (3x3)
Mean (3x3)
Mean (5x5)
Gaussian (0.006)
Poisson
Salt & Pepper
JPEG (10%)
JPEG (30%)
JPEG (60%)
JPEG (90%)
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
Figure 4.11: Extracted RTU logo watermarks by proposed method using DE
after applying considered attacks.
78
Extracted Aeroplane Watermark Image After Attacks
Median (3x3)
Median (5x5)
Wiener (3x3)
Mean (3x3)
Mean (5x5)
Gaussian (0.006)
Poisson
Salt & Pepper
JPEG (10%)
JPEG (30%)
JPEG (60%)
JPEG (90%)
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
Figure 4.12: Extracted Aeroplane watermarks by proposed method using DE
after applying considered attacks.
79
Moreover, different optimization methods are used for optimizing the strength
factors to improve the quality and robustness of the proposed method. The performance of the proposed methods have been measured in terms of quality and
robustness against different signal processing attacks and results are compared
with the work of Chou and Wu [96] and Su et al. [98]. The results validate that
the proposed method using DE is better than other methods for all the considered
parameters except slight decay in JPEG compression and rotation attacks as compared to Su et al. [98]. Therefore, it is concluded that the proposed method using
DE has high quality and robust results and can further be used for protection of
the copyrighted images.
The next chapter introduces SPT-based watermarking method for color watermark images.
80
Chapter 5
Digital Image Watermarking using Steerable Pyramid Transform on Color Watermark Images
5.1
Introduction
Previous chapter presented the DWT-based image watermarking method for protecting the color images against illegal uses by embedding the color watermark.
Further, optimization methods have been used to improve the performance. In
Chapter 3, the SPT-based image watermarking method has been proposed for
protecting the color images against illegal uses by embedding the gray-scale watermark. Moreover, analysis of the results show that the SPT-based method outperforms as compared to DWT. Therefore, this chapter proposes a method for
the protection of color images using SPT by embedding color watermark images.
Since, from the results of previous chapter, it is validated that DE outperforms
other optimization function, hence in this chapter, the DE has been used to optimized the strength factors.
Rest of the chapter is organized as follows. Section 5.2 describes the proposed
method. The method validation parameters and experimental results are discussed
in Section 5.3 and 5.4 respectively. Finally, Section 5.5 concludes the chapter.
5.2
Proposed Method
The proposed method explores the advantages of uncorrelated color space over
the correlated color space to improve the performance of watermarking methods
in terms of quality and robustness. It implants the color watermark image into
the color host image by modifying the decomposed SPT coefficients of host image. Each channel of color image is embedded in the corresponding channel of
host image to increase the reliability during the recovery process and to protect
against the common signal processing attacks. The proposed method consists
of five phases namely; pre-processing of host and watermark image, watermark
embedding, image post-processing, extraction of watermark, and performance improvement using DE. The basic structural design of the proposed method is shown
in Figure 5.1. The details of each phase of the proposed method are described in
the following sections.
Image Pre-processing
In image pre-processing, both the host RGB color image (H) and watermark
image (W ) are transformed into UCS color space using Eq. (2.8) which produces six independent image components (three for host and three for watermark)
namely; HU , HC , and HS for host image and WU , WC , and WS for watermark image. After transformation, each component of watermark image is divided into 16
non-overlapping sub-areas as shown in Figure 5.2. These sub-areas of watermark
image are then re-arranged or scrambled by some pre-defined sequence or key to
introduce one more level of security and enforced the user to use the key for the
extraction of the image from watermarked image. The pre-defined sequence or
key has 16 values (1-16) which are generated randomly. The watermark image is
scrambled according to this randomly generated sequence. One of the scrambled
pattern is shown in Image Preprocessing block of Figure 5.1. The above generated
pattern is stored for extraction phase. Further, third level of steerable pyramid
transformation decomposition is applied on each component of host image. The
resultant coefficients (Hk3 , k = {1, 2, 3, ..., 16}) is chosen for embedding process.
Watermark Embedding
After pre-processing of the images, the scrambled watermark is embedded into the
SPT coefficients of host image. In order to increase the reliability and robustness
of proposed method against the malicious attacks, it is desired to hide each color
component of the watermark image in corresponding component of host coefficients. Now, the scrambled watermark is embedded into approximation details of
the third level decomposed host image coefficients by using Eq. (5.1).
HWk3 (x, y) = Hk3 (x, y) + α(r)Wp (x, y)
k = {1, 2, 3, ..., 16}
p = {1, 2, 3, ..., 16}
r = {1, 2, 3, ..., 16}
82
(5.1)
Figure 5.1: Structural design of proposed method.
here, k represents the sub-areas of host image, p is the intended block of watermark, and α denotes the strength of modification done in the host coefficients.
Post-processing
After embedding all the color channels of UCS watermark image into corresponding SPT host image coefficients, inverse SPT (ISPT) is taken for watermarked host
83
Figure 5.2: Watermark division.
image coefficients which returns watermarked image in UCS color space. Finally,
UCS watermarked image is reconverted into RGB color space.
Watermark Extraction
The watermark extraction process requires watermarked image and pre-defined
key. To extract the watermark, RGB watermarked image is transformed into UCS
color space and then SPT decomposition on each channel is applied. To recover the
watermarked image, a reverse process of embedding is applied as shown in Figure
5.1 by using Eq. (5.2). Finally, the scrambled watermark image is rearranged to
its original sequence.
H (x,y)−H
(x,y)
Ŵp (x, y) = k3 α(r)Wk3
k = {1, 2, 3, ..., 16}
p = {1, 2, 3, ..., 16}
(5.2)
r = {1, 2, 3, ..., 16}
Performance Improvement using DE
The watermarked image is subjected to various watermarking attacks which degrade the performance of watermarking methods. The design of the apposite attacks are as important as to design a method for protection of multimedia contents
because they are required to test the robustness and security of the newly design
methods for protection of multimedia contents. There are mainly two parameters namely; quality and robustness which must be maximized for a watermarking
method, but these parameters are inversely related with each other i.e. if quality increases, robustness suffers and vice-versa. In this method, optimum values
of these parameters are dependent on the suitable values of strength factor (α).
Therefore, this chapter uses DE optimization method for selecting the values of α
which optimizes the quality and robustness in terms of fitness function.
84
This method uses following settings of DE parameters:
• The crossover probability CR = 0.9.
• The scale factor which controls the implication of the differential variation
F = 0.5.
• Population size NP = 50.
• The stopping criteria is set to the maximum number of iterations which is
500 for this experiment.
There are 16 strength factors used in this method which become the dimension
of each individual in DE method. The optimized values of these 16 strength factors
are calculated by minimizing the following fitness function :
90%
100
100
F itness =
[1 − NCjpg(Q) ]
+
+2×
CP SNR SSIM
Q=60%
+
50%
[1 − NCjpg(Q)] +
i=1
Q=10%
+
+
3
i=1
4
5
[1 − NCf ilter(i) ]
[1 − NCscale(i) ] +
3
(5.3)
[1 − NCnoise(i) ]
i=1
[1 − NCcrop(i) ] + [1 − NCrotation ]
i=1
here, CPSNR is composite peak signal-to-noise ratio, SSIM is structural similarity,
and NC is normalized correlation calculated for the attacks namely; JPEG compression (10 to 90%), filtering (wiener, mean, median), scaling (0.6 to 1.2 factor),
noise addition (poisson, salt and pepper, gaussian), rotation (10 ), and cropping
(10 to 40 %). These values are calculated by applying the mentioned attacks on
watermarked image.
5.3
Method Validation
The results obtained from the proposed method are compared with the methods
of Chou and Wu [96] and Su et al. [98] who also embedded the color watermark
images into the color host images. To compare these methods, quality and robustness parameters are considered. The quality of watermarked image can be
assess by two ways namely subjective and objective tests. Subjective test is done
by 10 humans beings on the scale of 0 (very poor) to 5 (excellent) while objective
85
test calculates the parameters namely CPSNR and SSIM. CPSNR measures the
degree of similarity between the original and watermarked image and is calculated
as follows [42]:
CP SNR =
M × N × 2552
10 log M N
2
3 s
m=1
n=1 [H(m, n, s) − HW (m, n, s)]
(5.4)
SSIM also measures the degree of similarity by including the three aspects of HVS
namely loss of correlation (c), luminance distortion (l ), and contrast distortion (s).
It is formulated as follows [98]:
SSIM = l(H, HW )c(H, HW )s(H, HW )
where,
(5.5)
l(H, HW ) =
2μH μHW + C1
μ2H + μ2HW + C1
(5.6)
c(H, HW ) =
2σH σHW + C2
2
2
σH
+ σH
+ C2
W
(5.7)
σHHW + C3
σH σHW + C3
(5.8)
s(H, HW ) =
here μ and σ are mean and standard deviation respectively while C1 , C2 , and
C3 are three positive constants used to avoid a null denominator. The values of
CPSNR and SSIM must be maximized for effective quality of watermarked image.
The robustness of watermarking method shows the degree of similarity between
the original watermark image and extracted watermark image after applying the
attacks and is measured by NC which is formulated as shown in Eq.5.9:
1
NC =
3 s
L
m=1
L
[W (m, n, s) × Ŵ (m, n, s)]
n=1
L
L
2
m=1
n=1 W (m, n, s)
(5.9)
The value of NC lies in the rage of 0 (no similarity) to 1 (similar) and is calculated
for the extracted watermark without attack and with attacks namely; JPEG compression (10 to 90%), filtering (wiener, mean, median), scaling (0.6 to 1.2 factor),
noise addition (poisson, salt and pepper, gaussian), rotation (10 ), and cropping
(10 to 40 %).
86
5.4
Experimental Results
To compare the performance of proposed and considered color image watermarking
methods, four popularly used 24-bit RGB color images namely; lena, mandrill,
pepper, and sailboat each of size 512 × 512 are used as host images which are
shown in Figure 5.3. In this chapter, one 24-bit RGB color logo and one RGB
color image namely; RTU logo and aeroplane color image respectively each of size
64 × 64 are used as watermark images which are shown in Figure 5.4. All the
considered images have been taken from USC-SIPI image database [112] except
RTU logo which is taken from Rajasthan Technical University, Kota, India.
(a) Lena
(b) Mandrill
(c) Pepper
(d) Sailboat
Figure 5.3: RGB host images.
(a) RTU logo
(b) Aeroplane
Figure 5.4: RGB watermark images.
In the pre-processing step of the proposed method, each host and watermark
images are converted into UCS color space followed by SPT decomposition of host
images and scrambling of watermark images. Figure 5.5 shows the converted UCS
images of host and watermark. The watermark image goes through the process
of scrambling. One of the possible scrambled watermark images of RTU logo and
aeroplane are shown in Figure 5.6. Now, the watermark images are embedded
into each of the considered host images and the resultant watermarked images are
shown in Figure 5.7.
A comparison of fitness values before and after optimization for 30 runs has
been depicted in Figure 5.8 which shows that the use of DE enhances the fitness
function value.
87
(a)
(b)
(c)
(d)
(e)
(f)
Figure 5.5: UCS host and watermark images (a). Lena, (b). Mandrill, (c).
Pepper, (d). Sailboat, (e). RTU Logo, and (f). Aeroplane.
(a) RTU logo
(b) Aeroplane
Figure 5.6: Representative scrambled UCS watermark images.
The performance of the proposed method is compared with method proposed
in the chapter 4, in which DWT and DE have been used to increases the quality
and robustness of image watermarking method. To compare the objective test for
quality measures of resultant watermarked images using proposed and considered
methods, CPSNR and SSIM performance parameters are calculated and depicted
in Table 5.1. From Table 5.1 it is validated that all the methods including proposed
method maintain the quality of the watermarked image in terms of CPSNR (>
35dB) and SSIM (> 0.95). However, the proposed method shows lower values of
CPSNR and SSIM as compared to other considered methods due to the hiding
of complete watermark into all the three channels of host image, while existing
methods hide in only one channel of the host image. Further, the results using
SPT is more promising than DWT-based method. Moreover, subjective test on
the resultant watermarked images is performed and presented in Table 5.2. The
results of subjective test show that the proposed method effectively embeds the
88
(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figure 5.7: RGB watermarked images embedded by (a)-(d) RTU logo and
(e)-(h) Aeroplane image
Figure 5.8: A comparison of before and after optimization of fitness values for 30
runs.
watermarks which is imperceptible by human beings.
To show the effectiveness of the proposed watermarking method, the extracted
watermarks from the watermarked images and their NC values are shown in Figure
5.9 for each of the considered method. From Figure 5.9, it is visualized that the
proposed method using UCS color space and Su et al. [98] have highest NC values
(1.0) for all extracted watermarks and hence outperforms the method of Chou and
Wu [96]. The columns of Figure 5.9 show extracted watermarks from considered
watermarked images and first four rows shows the extraction of RTU logo while
last four shows extraction of aeroplane image using the proposed and considered
89
Table 5.1: Comparison of CPSNR and SSIM values of watermarked images
resultant from proposed and considered methods.
Quality
Parameters
Watermarked
images
Su et
[98]
1.
CPSNR
2.
SSIM
Lena
Mandrill
Pepper
Sailboat
Lena
Mandrill
Pepper
Sailboat
36.94
36.78
36.98
36.89
0.98
0.97
0.97
0.97
S.No.
al.
Chou
and
Wu [96]
DWTbased
Proposed
Method
SPT-based
Proposed
Method
38.17
38.09
37.38
37.69
0.97
0.96
0.97
0.97
36.01
35.89
35.41
35.21
0.98
0.97
0.96
0.97
36.28
36.03
35.58
35.41
0.98
0.96
0.96
0.97
Table 5.2: Average subjective quality comparison of original and watermarked
images by 10 human beings in the scale of 0 to 5.
Watermark
image
Watermarked
image
Average Score of 10 Human beings
Su et al. Chou
and DWT[98]
Wu [96]
based
Proposed
Method
SPT-based
Proposed
Method
1.
RTU Logo
2.
Aeroplane
Lena
Mandrill
Pepper
Sailboat
Lena
Mandrill
Pepper
Sailboat
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
S.No.
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
methods of watermarking.
The robustness of the proposed method has been tested by applying different
attacks on the watermarked images. In this chapter, attacks have been categorized into two classes namely; common signal processing attacks and geometric
attacks. The considered common signal processing attacks consists of filtering attacks (mean, median, wiener), noise attacks (gaussian, poisson, salt and pepper),
and JPEG compression attacks while rotation, scaling, and cropping are the considered geometric attacks. The attacks are applied on all the eight watermarked
images embedded with RTU logo and aeroplane images. The comparison of NC
values after applying the common signal processing attacks on watermarked images embedded with RTU logo are depicted in Table 5.3 while Table 5.4 shows
the NC values for watermarked images embedded with aeroplane image by all
considered methods and proposed method. After applying the geometric attacks,
the measured NC values for both the watermark images are compared in Table
5.5. From Tables 5.3 – 5.5, it is observed that the robustness of watermarked
images embedded with RTU logo have higher values of NC as compared to aero90
Method
a. Lena
Used Watermarked Image
b. Mandrill
c. Pepper
d. Sailboat
Chou and Wu [96]
(NC)
0.98
0.99
0.99
0.98
1.00
1.00
1.00
1.00
1.00
0.99
1.00
1.00
1.00
1.00
1.00
1.00
0.99
0.98
0.99
0.99
1.00
1.00
1.00
1.00
1.00
0.99
1.00
0.99
1.00
1.00
1.00
1.00
Su et al. [98]
(NC)
DWT-based Proposed Method
(NC)
SPT-based Proposed Method
(NC)
Chou and Wu [96]
(NC)
Su et al. [98]
(NC)
DWT-based Proposed Method
(NC)
SPT-based Proposed Method
(NC)
Figure 5.9: Comparison of extracted watermarks by considered and proposed
methods along with their corresponding NC values. Columns shows extracted
watermarks from watermarked image namely (a). Lena, (b). Mandrill, (c).
Pepper, and (d). Sailboat using the considered and proposed methods mentioned
in first column. First four rows shows the extraction of RTU logo while last four
shows extraction of aeroplane watermark image.
91
92
Median
(5 × 5)
Wiener
(3 × 3)
Mean
(3 × 3)
Mean
(5 × 5)
Gaussian
(0.006)
Poisson noise
Salt and pepper
noise
JPEG compression (10%)
JPEG compression (30%)
JPEG compression (60%)
JPEG compression (90%)
2
3
4
5
6
7
8
9
10
11
12
noise
filtering
filtering
filtering
filtering
Median
(3 × 3)
1
filtering
Attacks
S.No.
0.44
0.34
0.21
0.14
0.91
0.87
0.91
0.41
0.54
0.68
0.11
0.18
Chou
and
Wu
[96]
0.94
0.91
0.89
0.72
0.92
0.84
0.83
0.88
0.97
0.88
0.63
0.74
Su
et
al.
[98]
0.91
0.88
0.86
0.70
0.94
0.86
0.84
0.90
0.99
0.90
0.65
0.75
DWTbased
Proposed
Method
lena
0.92
0.89
0.87
0.70
0.94
0.86
0.85
0.90
0.99
0.90
0.65
0.76
SPTbased
Proposed
Method
0.41
0.32
0.20
0.13
0.84
0.81
0.84
0.38
0.51
0.63
0.10
0.17
Chou
and
Wu
[96]
0.87
0.84
0.82
0.67
0.85
0.78
0.77
0.82
0.90
0.82
0.59
0.68
Su
et
al.
[98]
0.85
0.82
0.80
0.65
0.87
0.80
0.79
0.83
0.92
0.83
0.60
0.70
DWTbased
Proposed
Method
Mandrill
0.85
0.82
0.81
0.65
0.88
0.80
0.79
0.84
0.92
0.84
0.61
0.70
SPTbased
Proposed
Method
0.43
0.34
0.21
0.14
0.89
0.85
0.89
0.40
0.53
0.66
0.11
0.18
Chou
and
Wu
[96]
0.92
0.89
0.87
0.70
0.90
0.82
0.81
0.86
0.95
0.86
0.62
0.72
Su
et
al.
[98]
0.89
0.86
0.84
0.68
0.92
0.84
0.83
0.88
0.97
0.88
0.64
0.74
DWTbased
Proposed
Method
Pepper
0.90
0.87
0.85
0.69
0.92
0.84
0.83
0.88
0.97
0.88
0.64
0.74
SPTbased
Proposed
Method
0.43
0.33
0.20
0.14
0.87
0.83
0.87
0.40
0.52
0.65
0.11
0.17
Chou
and
Wu
[96]
0.90
0.87
0.85
0.69
0.88
0.80
0.79
0.84
0.93
0.84
0.61
0.71
Su
et
al.
[98]
0.87
0.85
0.83
0.67
0.90
0.82
0.81
0.86
0.95
0.86
0.62
0.72
DWTbased
Proposed
Method
Sailboat
0.88
0.85
0.83
0.67
0.90
0.82
0.81
0.86
0.95
0.86
0.63
0.73
SPTbased
Proposed
Method
Table 5.3: Comparison of robustness in terms of NC values obtained after applying common signal processing attacks on the
watermarked image embedded with RTU logo
93
Median
(5 × 5)
Wiener
(3 × 3)
Mean
(3 × 3)
Mean
(5 × 5)
Gaussian
(0.006)
Poisson noise
Salt and pepper
noise
JPEG compression (10%)
JPEG compression (30%)
JPEG compression (60%)
JPEG compression (90%)
2
3
4
5
6
7
8
9
10
11
12
noise
filtering
filtering
filtering
filtering
Median
(3 × 3)
1
filtering
Attacks
S.No.
0.40
0.27
0.16
0.11
0.83
0.71
0.82
0.30
0.42
0.51
0.06
0.13
Chou
and
Wu
[96]
0.92
0.90
0.87
0.70
0.85
0.82
0.84
0.88
0.96
0.84
0.63
0.72
Su
et
al.
[98]
0.89
0.87
0.84
0.68
0.87
0.83
0.86
0.90
0.98
0.86
0.64
0.73
DWTbased
Proposed
Method
lena
0.90
0.88
0.85
0.68
0.87
0.84
0.86
0.90
0.98
0.86
0.64
0.74
SPTbased
Proposed
Method
0.38
0.25
0.15
0.10
0.77
0.66
0.76
0.28
0.39
0.48
0.06
0.12
Chou
and
Wu
[96]
0.85
0.83
0.81
0.65
0.79
0.76
0.78
0.82
0.89
0.78
0.58
0.67
Su
et
al.
[98]
0.83
0.81
0.78
0.63
0.80
0.78
0.80
0.83
0.91
0.80
0.59
0.68
DWTbased
Proposed
Method
Mandrill
0.83
0.81
0.79
0.63
0.81
0.78
0.80
0.84
0.91
0.80
0.60
0.68
SPTbased
Proposed
Method
0.40
0.27
0.16
0.11
0.81
0.69
0.80
0.30
0.42
0.50
0.06
0.13
Chou
and
Wu
[96]
0.90
0.88
0.85
0.68
0.83
0.80
0.82
0.86
0.94
0.82
0.61
0.70
Su
et
al.
[98]
0.87
0.85
0.83
0.66
0.85
0.82
0.84
0.88
0.96
0.84
0.63
0.72
DWTbased
Proposed
Method
Pepper
0.88
0.86
0.83
0.67
0.85
0.82
0.84
0.88
0.96
0.84
0.63
0.72
SPTbased
Proposed
Method
0.39
0.26
0.15
0.11
0.79
0.68
0.78
0.29
0.41
0.49
0.06
0.13
Chou
and
Wu
[96]
0.88
0.86
0.83
0.67
0.81
0.78
0.80
0.84
0.92
0.80
0.60
0.69
Su
et
al.
[98]
0.86
0.84
0.81
0.65
0.83
0.80
0.82
0.86
0.94
0.82
0.61
0.70
DWTbased
Proposed
Method
Sailboat
0.86
0.84
0.81
0.65
0.83
0.81
0.82
0.86
0.94
0.82
0.62
0.71
SPTbased
Proposed
Method
Table 5.4: Comparison of robustness in terms of NC values obtained after applying common signal processing attacks on the
watermarked image embedded with Aeroplane image.
94
0.56
0.74
Cropping (20%)
Cropping (30%)
Cropping (40%)
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
6
7
8
1
2
3
4
5
6
7
8
0.43
0.63
0.77
0.87
0.68
0.60
0.51
0.71
0.82
0.92
Cropping (10%)
0.73
0.64
5
Scaling (0.9)
3
0.55
Scaling (1.2)
Scaling (0.6)
2
0.87
Chou
and
Wu
[96]
4
Rotation (10 )
Attacks
1
S.No.
0.62
0.68
0.80
0.92
0.98
0.98
0.95
0.91
0.61
0.73
0.83
0.94
0.98
0.98
0.96
0.94
Su
et
al.
[98]
0.63
0.69
0.81
0.94
1.00
1.00
0.97
0.89
0.62
0.74
0.84
0.96
1.00
1.00
0.98
0.92
DWTbased
Proposed
Method
lena
0.63
0.69
0.82
0.94
0.98
0.98
0.97
0.91
0.62
0.75
0.85
0.96
0.98
0.98
0.98
0.95
SPTbased
Proposed
Method
Su
et
al.
[98]
DWTbased
Proposed
Method
Mandrill
Proposed Chou
Method and
Wu
using
[96]
UCS
0.56
0.68
0.77
0.87
0.93
0.91
0.89
0.87
0.57
0.69
0.79
0.89
1.00
0.93
0.91
0.86
0.58
0.69
0.79
0.90
0.98
0.93
0.91
0.88
0.54
0.69
0.80
0.90
0.71
0.62
0.53
0.85
0.40
0.58
0.71
0.81
0.63
0.55
0.47
0.69
0.57
0.63
0.74
0.85
0.93
0.91
0.88
0.85
0.58
0.64
0.76
0.87
1.00
0.93
0.90
0.83
0.59
0.65
0.76
0.88
0.98
0.93
0.91
0.86
0.43
0.61
0.75
0.85
0.66
0.58
0.49
0.72
For Aeroplane Watermark Image
0.52
0.66
0.76
0.85
0.68
0.59
0.51
0.81
For RTU Logo Watermark Image
Chou
and
Wu
[96]
0.60
0.66
0.78
0.90
0.98
0.96
0.93
0.89
0.59
0.71
0.81
0.92
0.98
0.96
0.94
0.92
Su
et
al.
[98]
0.62
0.68
0.80
0.92
1.00
0.98
0.95
0.86
0.61
0.73
0.83
0.94
1.00
0.98
0.96
0.91
DWTbased
Proposed
Method
Pepper
0.62
0.68
0.80
0.92
0.98
0.98
0.95
0.89
0.61
0.73
0.83
0.94
0.98
0.98
0.96
0.93
SPTbased
Proposed
Method
0.42
0.60
0.74
0.83
0.65
0.57
0.48
0.71
0.53
0.68
0.79
0.88
0.70
0.61
0.52
0.83
Chou
and
Wu
[96]
0.59
0.65
0.77
0.88
0.96
0.94
0.91
0.87
0.58
0.70
0.80
0.90
0.96
0.94
0.92
0.90
Su
et
al.
[98]
0.60
0.66
0.78
0.90
1.00
0.96
0.93
0.84
0.59
0.71
0.81
0.92
1.00
0.96
0.94
0.88
DWTbased
Proposed
Method
Sailboat
0.61
0.67
0.79
0.90
0.97
0.96
0.93
0.86
0.60
0.72
0.81
0.92
0.97
0.96
0.94
0.90
SPTbased
Proposed
Method
Table 5.5: Comparison of robustness in terms of NC values obtained after applying geometric attacks on the watermarked images.
Extracted Watermark Image After Attacks
Median (3x3)
Median (5x5)
Wiener (3x3)
Mean (3x3)
Mean (5x5)
Gaussian (0.006)
Poisson
Salt & Pepper
JPEG (10%)
JPEG (30%)
JPEG (60%)
JPEG (90%)
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
Figure 5.10: Extracted RTU logo watermarks by proposed method using UCS
and DE after applying considered attacks.
95
Extracted Aeroplane Watermark Image After Attacks
Median (3x3)
Median (5x5)
Wiener (3x3)
Mean (3x3)
Mean (5x5)
Gaussian (0.006)
Poisson
Salt & Pepper
JPEG (10%)
JPEG (30%)
JPEG (60%)
JPEG (90%)
Rotation (10 )
Scaling (0.6)
Scaling (0.9)
Scaling (1.2)
Cropping (10%)
Cropping (20%)
Cropping (30%)
Cropping (40%)
Figure 5.11: Extracted Aeroplane watermarks by proposed method using UCS
and DE after applying considered attacks.
96
plane image due to the coarseness of aeroplane image as compared to RTU logo.
The comparative results show that the proposed method using SPT, UCS color
space, and DE method outperforms other methods for all the considered attacks
except JPEG compression where the method of Su et al. [98] shows slightly better
robustness. The similar performance of the proposed method can be observed
from Figures 5.10 and 5.11 where the extracted RTU logo and aeroplane image
watermarks by proposed method using SPT, UCS, and DE for all the considered
attacks have been depicted. Therefore, it is validated from the results that the
proposed method using SPT, UCS color space, and DE produces high quality and
better robust watermarked images and can be utilized for content authentication
to protect the copyrighted images.
5.5
Results and Discussions
This chapter proposes a novel SPT-based color image watermarking method using
UCS and DE method. The use of uncorrelated color space increases the effective
utilization of all color channels of host image which is not feasible in correlated
color spaces while DE is used for optimizing the strength factors to improve the
quality and robustness of the proposed method. The performance of the proposed
method has been measured in terms of quality and robustness against different
signal processing attacks and results are compared with the work of Chou and
Wu [96] and Su et al. [98]. The results validate that the proposed method is
better than other methods for all the considered parameters except slight decay
in JPEG compression attack as compared to Su et al. [98]. Moreover, the results
are also compared with DWT-based method introduce in Chapter 4. The results
depict that SPT-based method outperforms DWT-based method. Therefore, it
is concluded that the proposed method using SPT, UCS color space, and DE
has high quality and robust results and can further be used for protection of the
copyrighted images.
97
Chapter 6
Conclusions and Scope for Future Work
In this thesis, an attempt has been made to identify the problems pertaining to
image watermarking for protection of color images. Color spaces, transform methods, optimization methods, and attacks are the major problems encountered in
the image watermarking area. In this work, some inherent drawbacks of existing methods used for quantitative analysis of watermarking for color images are
studied. The outcome of this study motivated to develop an image watermarking
system for the content authentication of color images.
Though the experimental results along with the discussions have been given
at the end of each chapter, this concluding chapter is mainly devoted to the contributions made in this thesis. The main contributions in this thesis are four-fold.
First, pre-process the input color images (host and watermark) by transforming it
into UCS color space. Second, four efficient transform-based image watermarking
methods for color images have been proposed to enhance the transparency and
robustness of method. Third, three optimization methods have been used to improves the performance of proposed methods by optimally selecting the strength
factors of watermarks and then post-processed the watermarked coefficients to reconstruct the watermarked image. Fourth, applying various watermarking attacks
in order to test the proposed method on various benchmark/ validation parameters
like composite-peak-signal-to-noise ratio (CPSNR), structural similarity (SSIM),
and normalized correlation (NC). Finally, an image watermarking method is designed and developed using the proposed methods for protection of color images.
The thesis is concluded with a summary of contribution followed by some pointers
to new research that are directly related to the present work.
6.1
Contributions Made in the Thesis
The major contributions of this work are discussed below.
1. Digital Image Watermarking using Discrete Wavelet Transform on
Gray-Scale Watermark Image
The first contribution is to develop a robust image watermarking method
based on the DWT and UCS for the protection of color images by embedding
the gray-scale image watermarks. Further, the capabilities of GA have been
used to optimize 16 strength factors to improves the transparency and the
robustness of proposed method. In this method, the watermark images
are embeds in third level decomposed image by using symlet-4 filter bank.
Experimental results show that the proposed method has good quality and
robustness against the 16 considered signal processing and geometric attacks.
Moreover, the performance of the proposed method is better than the image
watermarking method by Vahedi et al. [42] who also used DWT and GA
method.
2. Digital Image Watermarking using Steerable Pyramid Transform
on Gray-Scale Watermark Images
The second contribution is devoted to design an image watermarking method
based on the SPT and UCS. In this method a gray-scale watermark has been
embedded in third level decompose coefficients of input image. Furthermore,
GA has been used to enhance the performance of the method. Experimental results show that the proposed method has good quality and robustness
against the 16 considered signal processing and geometric attacks. Moreover,
the performance of the proposed method is better than the image watermarking method by Vahedi et al. [42] and the previously proposed method
(Digital Image Watermarking using Discrete Wavelet Transform on GrayScale Watermark Image) which also used DWT and GA in their method.
Hence, it is concluded that the SPT outperforms the DWT.
3. Digital Image Watermarking using Discrete Wavelet Transform on
Color Watermark Images
The third contribution introduces a robust and blind image watermarking
method based on DWT and UCS for the protection of color images by embedding the color image watermark into the third level decomposed host
coefficients. Moreover, to increase the reliability the watermark hides into
the multiple sections of the host image. Further, the capabilities of three
100
optimization methods namely; GA, ABC, and DE, have been exploited to optimize the 16 strength factors. Experimental results show that the proposed
method with DE has better quality and robustness against the 20 considered signal processing and geometric attacks. Moreover, the performance of
the proposed method is better than the image watermarking methods proposed by Su et al. [98] and Chou and Wu [96] who also embedded the color
watermark.
4. Digital Image Watermarking using Steerable Pyramid Transform
on Color Watermark Images
The fourth contribution is based on the SPT-based image watermarking
method for the protection of color host images by embedding the color watermark into the third level decomposed of image. Moreover, to increase
the reliability, the watermark is embedded into the multiple areas of the
host image. Further, the capabilities of the newly introduce DE method
has been exploited to optimize the 16 strength factors. Experimental results
show that the proposed method has better quality and robustness against
the 20 considered signal processing and geometric attacks. Moreover, the
performance of the proposed method is better than the image watermarking methods proposed by Su et al. [98], Chou and Wu [96], and previously
proposed method (Chapter 4) who have also embedded the color watermark.
6.2
Scope for Future Work
The present work opens a lot of new avenues and directions for research in the
field of watermarking. Some of the possible directions for future research related
to the present work are given below;
• The developed methods may be explored for recently introduced transforms
and color spaces.
• The proposed work can be further explored by using hybrid methods like
more than one transform, color space, etc.
• The present work can be further investigated for other multimedia contents
such as video signals and 3-D models.
• The developed methods may be further explored for other newly introduced
optimization methods.
101
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List of Publications
1. M. Gupta, G. Parmar, R. Gupta, and M. Saraswat, “Discrete wavelet
transform-based color image watermarking using uncorrelated color space
and artificial bee colony”, International Journal of Computational Intelligence Systems, Taylor & Francis, Vol. 8 (2), pp. 364-380, 2015.
[Published]
2. M. Gupta, G. Parmar, R. Gupta, and M. Saraswat, “Color image watermarking using steerable pyramid transform and uncorrelated color space”,
Journal of Experimental and Theoretical Artificial Intelligence, Taylor &
Francis. [Under Review after Third Minor Revision]
3. M. Gupta, G. Parmar, R. Gupta, and M. Saraswat, “Digital image watermarking using steerable pyramid transform and uncorrelated color space”,
in Proc. of Ninth International Conference on Industrial and Information
Systems (ICIIS 2014), India, 2014.
[Published]
4. M. Gupta, G. Parmar, R. Gupta, and M. Saraswat, “Digital image watermarking using uncorrelated color space”, in Proc. of IEEE Symposium
on Computer Applications & Industrial Electronics (ISCAIE 2014), Penang,
Malaysia, 2014.
[Published]
5. M. Gupta, G. Parmar, R. Gupta, and R. Saraswat, “Authentication of
Multimedia Assets using Digital Watermarking: A Review”, in Proc. of
International Conference on Electronic Communication & Instrumentation
(e-Manthan 2012), India, 2012. [Published]
115