DIGITAL IMAGE WATERMARKING BY USING INTERMEDIATE
SIGNIFICANT BITS IN GRAY SCALE IMAGES
ALI SHARIFARA
A dissertation submitted in partial fulfillment of the
requirements for the award of the degree of
Master of Science (Computer Science)
Faculty of Computer Science and Information Systems
Universiti Teknologi Malaysia
AUGUST 2012
To my lovely father, AliAkbar Sharifara to support and encourage me in the whole of
my life specially for being my source of inspiration…
Thanks for being there & no words could
describe your support & encouragement
I love you…
ACKNOWLEDGEMENT
My appreciation first of all goes to my supervisor, Prof. Ghazali Bin Sulong,
I would like to take this opportunity to thank him who gives me a lot of
encouragement and guidance during the dissertation. His guidance has been a
valuable asset for me during this dissertation.
I also would like to thank all my friends who are given appreciated opinion
and idea of this dissertation. They are also willing to share their knowledge with me.
All the discussion and recommended opinion of my course mare and friends were
appreciated by me.
Finally, I would like to send my appreciation to my lovely family for their
support and encouragement throughout my whole life.
ABSTRACT
The rapid growths of the computer technologies have been increased over the
last half century in terms of amount and complexity of data. Also, access to the
data has become much easier due to rapid growth of the networks such as
Internet. Furthermore, most of the people use image to represent information and it is
transferred throughout the internet. Digital watermarking techniques are used to
protect the copyrights of multimedia data by embedding secret information inside
them, for example, embedding in images, audios, or videos. Digital Image
watermarking also has been used to detect original images against forged images by
embedding an evidence of the owner of image. Digital Image watermarking can be
categorized into two domains namely; spatial, and frequency domains. The former
has been chosen for the current research and some remaining problems have been
studied such as imperceptibility, security, and robustness of watermarked image. A
repeated method is proposed to solve the problem of robustness, and a Zig-zag
algorithm is also recommended to change the order of embedding in watermarked
image. Furthermore, the proposed method aims to embed watermark in different bit
planes of host image which can improve both the visual quality and robustness. To
evaluate the proposed technique, some attacks have been applied for the approach
and the
experimental
results have
shown
that
the
proposed
technique
successfully withstood against most of the attacks, and at the same time preserved
the watermarked image quality.
ABSTRAK
Kadar tumbesaran teknologi komputer telah melonjak naik sejak dari separuh abad
yang lalu dari segi aspek perubahan jumlah dan kerumitan data. Juga, capaian data
menjadi lebih mudah dengan kemajuan pesat jaringan seperti internet. Selanjutnya,
kebanyakkan orang lebih cenderung menggunakan imej sebagai medium utama
untuk menyampaikan maklumat melalui internet. Teknik Tera Air digunakan untuk
menlindungi hak milik data multimedia dengan membenamkan maklumat rahsia ke
dalamnya, sebagai contoh, pembenaman dalam gambar, audio dan video. Tera Air
digital juga digunakan dalam mengesan keaslian imej daripada yang palsu dengan
membenamkan bukti pemilik asal imej tersebut. Tera Air digital boleh di kategorikan
dalam 2 domain iaitu spatial dan frekuansi. Domain spatial dipilih dalam kajian ini
dan masalah yang masih wujud ini dikaji seperti ketampakan, keselamatan dan
kekukuhan imej tera air . Kaedah pengulangan dicadangkan untuk menyelesaikan
masalah ketampakan dan algoritma zig-zag dicadangkan untuk mengubah susunan
pembenaman imej tera air. Seterusnya kaedah yang dicadangkan ini mensasarkan
untuk membenamkan tera air dalam satuan ‘bit’ yang berlainan pada imej luas
supaya dapat meningkatkan kualiti visual dan kekukuhan pada tera air tersebut.
Untuk menilai kaedah yang dicadangkan ini, beberapa serangan telah dilakukan dan
keputusan
kajian
telah
menunjukan
bahawa
kaedah
tersebut
berjaya
mempertahankan hampir keseluruhan serangan dan pada masa mengekalkan kualiti
imej tera air.
TABLE OF CONTENTS
CHAPTER
1
2
TITLE
PAGE
DECLARATION
ii
DEDICATION
v
ACKNOWLEDGEMENT
vi
ABSTRACT (ENGLISH)
vii
ABSTRACT (MALAY)
viii
TABLE OF CONTENTS
ix
LIST OF TABLES
xiii
LIST OF FIGURES
xiv
LIST OF ABBREVIATIONS
xvi
INTORDUCTION
1
1.1
Introduction
1
1.2
Background of the Problem
3
1.3
Statement of the Problem
4
1.4
Aim of dissertation
5
1.5
Objectives of the Study
5
1.6
Scope of the Study
6
1.7
Significance of the study
7
1.8
Dissertation organization
7
LITERATURE REVIEW
8
2.1
8
Introduction
2.2
Digital Watermarking history
11
2.3
Digital watermarking overview
13
2.4
Importance of digital watermarking
13
2.5
Applications of watermarking
14
2.5.1
Ownership assertion
14
2.5.2
Fingerprinting
14
2.6
2.7
Embedding Watermark Techniques
15
2.6.1 Visible watermarking
15
2.6.2
16
Invisible watermarking
Extraction of watermark
17
2.7.1 Non-blind-watermarking
17
2.7.2
18
Blind-watermarking
2.8
Peak Signal-to-Noise Ratio (PSNR) Test
18
2.9
Measuring performance of watermarking
18
2. 9.1 Robustness
19
2. 9.2 Imperceptibility
19
2.9.3 Security
19
2.10
Watermarking Attacks
20
2.10.1 Classification of attacks
20
2.10.1.1 Removal attacks
21
2.10.1.2 Geometric attacks
21
2.10.1.3 Protocol attacks
22
2.10.1.4 Cryptographic attacks
23
2.11
2.12
Spatial Domain Methods
23
2.11.1 Least Significant Bit
24
2.11.2 Most Significant Bit
26
Frequency Domain Methods
26
2.13
3
28
RESEARCH METHODOLOGY
31
3.1
Introduction
31
3.2
Research environment
32
3.3
Introduction of the new method
33
3.3.1
33
The best quality of image
3.3.2 The best robustness
34
3.3.3 Variable Repetition
36
3.4
Integrate watermarking
36
3.5
Security in watermarking
37
3.5.1
Random Pixel Manipulation methods
38
3.5.2
Zig-zag embedding matrix
39
3.6
4
Related work
Enhancement of capacity in watermarked
Image by PVD
40
3.7
Evaluation of Quality and Robustness
41
3.8
Embedding process
42
3.9
Embedding Phase
43
3.10
Extracting Phase
44
3.11
Applying attacks
45
3.12
Dissertation Framework
46
3.13
Summary
47
IMPLEMENTATION
48
4.1
Introduction
48
4.2
The chosen attacks
49
4.3
The implementation
50
4.4
Evaluation of watermarking methods
51
4.4.1 Mean Squared Error (MSE)
51
4.4.2
51
Pick Signal to Noise Ratio (PSNR)
4.5
Watermarking with the best quality
52
4.6
Watermarking with improve security
52
4.7
Implementation and result
54
4.8
Improving the capacity
67
4.9
Summary
67
5
CONCLUSION
69
5.1
Introduction
69
5.2
Contribution and Achievement
69
5.3
Future Work
71
5.3
Summary
72
REFERENCES
73
LIST OF TABLES
TABLE NO
TITLE
PAGE
2.1
Intensity Matrices
29
4.1
The impact of attacks on Images per pixel
50
4.2
The watermarked images by using proposed
method for each bit plane and their PSNR value (Lena)
4.3
The watermarked images by using proposed method
for each bit plane and their PSNR value (Lake)
4.4
56
The watermarked images by using LSB until MSB
for each bit plane and their PSNR value (Lena)
4.5
55
57
The watermarked images by using LSB until MSB
for each bit plane and their PSNR value (Lake)
58
4.6
NCC value of proposed method
59
4.7
NCC Value for LSB until MSB under different attacks
60
4.8
NCC value for LSB until MSB under different attacks
60
4.9
Extracted watermark after attack in proposed method (Lena)
61
4.10
Extracted watermark after applying attacks in proposed method
62
4.11
Extracted watermark after applying the attacks in LSB method
63
LIST OF FIGURES
FIGURE NO
TITLE
PAGE
1.1
The approach of study
6
2.1
Sample of analog watermarking
9
2.2
Embedding watermark process
10
2.3
Detecting watermark process
10
2.4
Extracting watermark process
10
2.5
Watermarking Schemes
11
2.6
Visible image watermarking
15
2.7
Invisible image watermarking
17
2.8
Watermarking Attacks
20
2.9
8 bit planes of a gray scale image
22
2.10
LSB and MSB bit-Planes
24
2.11
pixel value of the cover image and watermark
25
2.12
Digital watermarking based on DCT
27
2.13
pixel intensity matrixes
29
3.1
Grayscale watermark and host images
32
3.2
Eight layers of host and watermark images
33
3.3
Divided cover image into blocks
34
3.4
Repeating bits in each block
35
3.5
different sizes of blocks
36
3.6
Random Pixel Manipulation process
38
3.7
Zig-zag embedding matrix
39
3.8
Capacity improvement techniques
41
3.9
Embedding watermark phase’s framework
43
3.10
Extracting watermark phase’s framework
44
3.11
Applying attack phase’s framework
45
3.12
Dissertation Framework
46
4.1
Original Host image and different attacks
49
4.2
Improving security by using Zig-zag algorithm
53
4.3
Applying zig-zag algorithms to embed watermark
54
4.4
Applied salt and pepper attack for both proposed and
LSB method
64
4.5
Applied Gaussian attack for both proposed and LSB method
65
4.6
Applied Speckle attack for both proposed and LSB method
65
4.7
Applied Poisson attack for both proposed and LSB method
66
4.8
Applied blurring attack for both proposed and LSB method
66
LIST OF ABBREVIATIONS
LSB
-
Least significant Bit
MSB
-
Most significant Bit
ISB
-
Intermediate Significant Bit
CWT
-
Continues Wavelet Transform
DFT
-
Discrete Fourier Transform
DCT
-
Discrete Cosine Transform
HVS
-
Human Visual System
PSNR
-
Peak Signal to Noise Ratio
MSE
-
Mean Squared Error
NCC
-
Normalized Cross Correlation
CHAPETR 1
INTRODUCTION
1.1 Introduction
Over the last half century the pace of change in the digital technologies has
been widely increased. Digital images as one of the digital technologies also have
been replaced with the analog images. Moreover, Internet also became as one of the
most important tools to transfer digital images from one part to other parts of the
world. For this reason, the security of digital documents became a challenging
concern and digital image watermarking as a solution is use to decrease the number
of digital forged documents (Langelaar, 2000; Kumar N.M. , 2011 ).
Information hiding methods have been using since the presence of paper and
after that used to many application areas such as digital image, audio, video which
put an evidence of the owner of documents such as a logo, serial numbers and etc
(Moulin, 2003). This might be helpful to decrease the number of unauthorized copy
of them. In addition, information hiding must be imperceptible and the signals of
embedded data must be low enough when projected into the human eyes.
Digital watermarking as a solution has been presented to be very practical for
identifying the owner of documents (Yongjian Hu, 2004). It has been presented for
some purposes like: copyright protection, data authentication, fingerprint, medical
applications, and broadcast monitoring. Furthermore, documents can be divided into
two main groups, analog and digital.
Image watermarking is the process of embedding an image into a host image.
For Instance, watermarks are embedded in bank cheque for preventing forgery.
Consequently, unauthorized modification of data is the concern of researchers about
copyright of documents and numerous image watermarking methods have been
proposed with different complexity levels so far, and all of them try to set up the
balance between quality and robustness of images.
Moreover, for extracting watermark there are two general approaches. The
first one is blind watermarking and the second one is non-blind watermarking
(Hyeong-In Choi, 2010). In blind watermarking there is no need to have the original
document, while in the non-blind extraction we need to have the original document
to detect the watermark (Song-Hwa Kwon, 2011). In addition, watermark can be
visible or invisible in both digital and analog documents. As an example, a visible
watermark can be a signature in an image that is used to point out the ownership of
image; meanwhile an invisible watermark is not apparent easily. The embedded
watermark can extract by using an extraction algorithms for identifying the copyright
owner.
Existing digital watermarking techniques can be categorized into one of the
two domains including spatial and frequency, according to the embedding domain of
the host image. There are lots of researches that have been proposed base on
frequency and spatial domains. For example, digital image watermarking by
applying LSB (Least Significant Bit) and MSB (Most Significant Bit) are two
common techniques that try to achieve robustness and quality at the same time in
spatial domain(Ibrahim Nasir, 2007 ).
1.2 Background of the Problem
There are lots of images on the internet without having watermark, and
everybody can download and modify them illegally. Furthermore, the owner of the
image can be missed without watermarking (Nour El-Houda Golea, 2010).
Consequently, watermarking is used to protect this behavior by adding an image as a
watermark into the host image. Regardless of images we also can use it for other
important documents such as video, audio, and text. Generally, there are some
concerns about this area of research that still need to be solve, such as
imperceptibility, robustness, security (Jiang Nan, 2006).
The first concern about the digital image watermarking is imperceptibility. It
means a watermark can be inserted in the cover image without making any kind of
degradation. It means, after embedding the watermark, the watermarked image and
host image must be identical. In other words, the watermarked image must be the
same as original image and nobody can realize the differences between them by the
naked eye. In fact, the embedded watermark is really imperceptible if human eyes
cannot distinguish between watermarked image and the original image.
The second one is robustness; it means the watermarking scheme must be
able to protect a watermark against geometric distortion attacks like rotation, JPEG
compression, and also signal processing attack like sharpening, blurring, adding
noise and so on. Robustness related to ability of recovering the watermark after
performing various processing attacks on watermarked image. The robustness must
be sufficient if any kind of attacks occurs. The watermarking scheme should be able
to protect watermark against possible signal processing operations, in addition for
evaluating the quality of the watermark after applying attacks.
Security is another important issue in watermarking, which means protecting
the watermark from unauthorized users and a digital watermark must be completely
invisible. Due to use algorithms for embedding the watermark into the cover image
and extract it as well, presence of a secret key is needed for keeping those algorithms
safe; otherwise unauthorized users can detect, remove, or modify the watermark form
the host image easily.
Moreover, the human eyes have different sensitivity for different image
regions and a watermark should embed in the highest priority part of the host image.
In the processing of watermarking, it is important to detect the best parts of the
image and embed the watermark within these areas of images.
Robustness and imperceptibility are two main parameters in the watermarking
that tries to adjust beside the capacity of information which can be embedded into the
host image (Ching-Tang Hsieh 2001).
1.3 Statement of the Problem
Illegal copying, modifying and copyright protection have become very
significant issues with the quick use of internet and no one can deny the importance
of Internet in our lives. Moreover, everyone can access to the vast of information on
the Internet that includes image, video, audio and text formats and also use them for
personal or commercial goals. Hence, malicious users can abuse of these digital
documents. So, digital watermarking can protect them from forgery. Therefore, the
digital watermarking methods have been identified as a possible solution to the
copyright protection, and have become an area of increased research activity over the
last decade (Gang Liu 2010).
Digital watermarking should be satisfied the below questions:
1- How we can embed an image as a watermark into the host image without
causing visible degradation?
2- How we can achieve quality of watermarked image without losing
robustness?
3- How much information we can put into the host image?
4- How we can extract watermark from the watermarked image?
1.4 Aim of dissertation
The main aim of this research is to propose a technique based on ISB
(Intermediate Significant Bit) to achieve high robustness and imperceptibility of gray
scale watermarked image. Applying ISB in watermarking is robustness enough to
protect image from attacks and also preserve quality of watermarked image in spatial
domain.
1.5 Objectives of the Study
To answer the problem statement, following objectives of this project are:
1- To study and apply watermarking in gray scale image by using ISB
(Intermediate Significant Bits).
2- To improve the robustness of gray scale image watermarking.
3- To apply image attacks and evaluate the quality and robustness of proposed
watermarking method by using PSNR (Peak Signal to Noise Ratio) and NCC
(Normalized Cross Correlation).
4- To compare proposed method to LSB and MSB methods after and before
applying attacks.
1.6 Scope of the Study
In this paper we deal with gray scale images as host image that we want to
add and extract watermark, and the approach is using non-blind watermarking in
spatial domain by applying ISB. The applied attacks are Gaussian filter, salt and
pepper, speckle, and Blurring attacks. Host image and watermark images are a grey
scale image with 256 × 256 pixels and a gray scale logo with 50 × 50 pixels,
respectively and both of the images are in TIF format.
Figure 1.1
The approach of study
1.7 Significance of the study
Over the past few years digital watermarking has became more popular due to
its significance in content authentication and legal ownership for digital multimedia
data. Furthermore, digital watermarking and data hiding has become an important
tool for protecting digital images from theft, illegal copying and unlawful
reproduction.
1.8 Dissertation organization
This research consists of five chapters. The first chapter presents introduction
to the project which includes the problem background, problem statements, aim of
project, the main objectives and scope of the project. In the chapter two covers
information about literature review on watermarking, which focused on current
algorithms that have been using for protecting image against attacks. The project
methodology is discussed in Chapter three where comparative study and pre-lab
testing have been used as the research strategy. In Chapter 4, the implementation of
the methodology where the findings of comparative study and pre-lab testing take
place and eventually, in Chapter five the result and findings of the lab testing is
explained and the overall project will concluded as well.
CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
The growth of the Internet over the last several years has been highlighted the
requirement of techniques for protecting ownership of digital documents in the
digital world and also identical copies of digital documents such as images, video,
and audio can distribute on the internet easily (Arya, 2010). Hence, without having
the powerful techniques for protecting digital documents, it is impossible to say who
the owner is and who the plagiarist is.
In consequence of all above reasons, digital watermarking is one of the proper
solutions for protecting copyright of digital documents against forgers. By using
watermarking in digital documents, we can protect of misusing behavior in important
documents like Will, Cheque and etc. The figure 2.1 depicts the watermark into the
money as an analog document which has been used to protect money against forgers.
Figure 2.1
Sample of analog watermarking
Embedding a hidden stream of bits in an image is called digital image
watermarking. The main aim of digital image watermarking is embedding image as a
watermark into an image as a host image and can be extracted later which provides
evidence of its authenticity. Hence, using watermark allows someone to identify the
original owner.
Digital image watermarking categorized into two main groups. They can be
visible or invisible (Arya, 2010). However, more focus is on invisible watermarking.
Due to, they don’t have any effects on the quality of images and watermarks in this
group are imperceptible as well. In visible watermarking the watermark can be seen
with the naked eyes, whereas in invisible methods, the watermark cannot be seen
easily and we need to use an extraction algorithm to take out the watermark from the
watermarked image. In fact, using each group of watermarking depends on their
applications. For example, television channels are using a visible watermark as a
logo that specifies the owner of channels.
There are three main processes involved in digital image watermarking which
includes embedding, detection, and extraction of a watermark (S. Shefali, 2007). We
also can secure watermarked image by adding a secure or public key in
watermarking process. It causes that only authorized receptions can extract
watermark form the digital content .In the figures 2.2-2.4, the main processes for
producing a watermarked image have been demonstrated.
Figure 2.2
Embedding watermark process
Figure 2.3
Detecting watermark process
Figure 2.4
Extracting watermark process
Digital image watermarking algorithms can divide into spatial and frequency
domains (K.Ganesan, 2010). There are many methods for achieving the goal have
been proposed in both spatial and frequency domains. In the rest, we will describe
other techniques which have been using to overcome the watermarking problems.
However, the main aim of this research is to achieving robustness and quality of
watermarked image in spatial domain.
2.2 Digital Watermarking history
Data hiding is a global term that encloses a wide range of problems beyond
that of embedding information in documents. The term of hiding here means trying
to keep the watermark imperceptible and keeping the information secure as well.
Hence, the data hiding has been divided into watermarking and steganography.
Figure 2.5
Watermarking Schemes
As we can see in the figure 2.5, data hiding can divide into watermarking and
steganography (Cox, 2008). Furthermore, the word of Steganography invented by
Trithemius and he was the first author of publications about cryptography. In
addition, this word derived from Greek words Steganos and Graphia which means
covered and writing respectively. In fact, Steganography is the art of hiding
communication.
In the late 20th century digital systems had become popular for the
watermarking of several documents. The main focus was on audio, video and
photographs. After that, when the attack occurred on September 11, 2001 Interest in
steganography increased, when it became clear that means for camouflaging the
communication itself are likely to be used for criminal actions. The first steganalytic
techniques focused on the most usual type of hiding which called LSB (Least
Significant Bit) embedding in bitmap images. After that, other types of images like
GIF, JPEG, etc beside audio formats had become popular (Cox, 2008).
Furthermore, the concern over copyright protection of documents was the
main reason to increase in watermarking interest. The internet had become more
popular by with the first web browser in 1993 and people wanted to download digital
media like videos, music and pictures form the internet. Moreover, internet is a
distribution system with high performance and cheap price in comparison by other
communication methods (Cox, 2008).
On the whole, due to need for copyright protection, digital watermarking
technology getting more and more popular along with associates to digital data
over the world that contains different multimedia documents such as image, video,
and audio.
2.3 Digital watermarking overview
The art of embedding copyright information into original contents is called
Digital watermarking and the information embedded is also called “watermarks”.
Digital watermarks usually do not put an obvious object in the content and do not
influence its appearance. Moreover, these are imperceptible and only can detect by
suitable authorities.
In fact, digital watermarking is a signal which carrying data that is embedded
into another signal, for example into an image or video signal. In other words, the
basic idea behind the digital watermarking is addition of a signal into another signal
which is imperceptible and also cannot be identified without having the knowledge
of the parameters of the watermarking algorithm.
2.4 Importance of digital watermarking
Due to digital broadcast of contents on the networks (especially Internet),
legal issues of copyright protection have become more important in the last several
years. Moreover, all of the information on the internet is provided as digital contents.
Hence, a desirable technique for protecting copyright is digital watermarking.
Adding a watermark which can authenticate the legal copyright owner and that
cannot be removed and manipulated easily by forgers without having the secure key
or other security restrictions. For instance, if cheque could be duplicated easily, trust
in its authenticity would be greatly decreased. For this reason, watermarks are
employed in currencies to reduce the risk of forgery as well. However, watermark is
not only the technique that has been using for preventing forged and illegal use.
As a result, digital watermarking protects the ownership of host signal by
embedding data such as the copyright data , company logo or any other digital data
that specifies the original owner of content. In addition, Watermarked object will be
produced when we embed watermark signal into the cover object.
2.5
Applications of watermarking
Digital Watermarks are potentially useful in many applications such as
ownership assertion, fingerprinting, medical industry, etc. in the rest; we described
some of them in detail.
2.5.1 Ownership assertion
Watermarks can play a role as evidence for the owners of the digital contents.
In other words, watermarks have been using in digital objects for protecting of the
ownership of the contents. For asserting ownership of a digital content like image, a
watermark signal can be generated by using an algorithm and also can be embedded
it into the host image. By doing this only the receiver and recipient of the digital
content can realize and extract the watermark by using the suitable algorithm.
2.5.2 Fingerprinting
Fingerprinting is one type of watermarks which recognizes the receiver of a
digital object as well as its owner. In other words, fingerprinting provides indication
of the ownership, and indication of the identity of a licensed user, by embedding
information in the digital content. This information can be imperceptible or
perceptible. Furthermore, Fingerprinting attempts to provide copyright owners with
the desired degree of protection.
2.6
Embedding Watermark Techniques
There are two fundamental ideas for embedding watermark into the digital
content. The watermark can be embedded in the digital content as visible or invisible.
2.6.1 Visible watermarking
A visible watermark is a visible content which is called watermark that covers
on the original content. In general, visible watermarking should be easily visible and
must be difficult to remove (Yongjian Hu, 2004).
A visible watermark is like a number or an image which is used to specify the
ownership of the content and everybody can see the watermark in the digital content
easily even with naked eyes. This technique allows the original content to be showed,
but it still provides evidence by marking the content as its owner’s possession. As an
example, in the figure 2.6 we demonstrated how visible image watermarking works.
In the below figure, (a) is a cover image and (b) is a watermark. The watermarked
image is also has been shown in figures 2.6 (c).
Figure 2.6
Visible image watermarking
Although we can use some techniques to make it difficult for removing
watermark form the original content, but we have to accept that remove the
watermark is not impossible (Yongjian Hu, 2004).
As a result, visible watermarking is a good solution for digital contents which
are distributed on the public networks like Internet. Moreover, users can realize the
main owner of digital content with naked eye and it could decrease the security of
watermarked image.
2.6.2 Invisible watermarking
Most of the data hiding techniques try to manipulate the cover digital objects
in order to can embed the secret data into them. Although, manipulate may be small
and imperceptible. Invisible watermarking is also one of data hiding techniques
which applied to digital contents like: image, video and audio.
In addition, invisible image watermarking embeds watermark into the cover
image in order to alter the pixel values without changing the quality of cover image
and nobody can notice it easily. Moreover, watermark can be extracted only with
proper decoding algorithm. The following figure shows invisible image watermark
process.
Figure 2.7
invisible image watermarking
There are two fundamental approaches for extracting watermark. The
watermark can be extracted from the digital content as non-blind or blind
watermarking. Depending on the method used for watermark extraction digital
watermarking can be divided into blind watermarking and non-blind watermarking
(Kumar N.M. , 2011 ).
2.7.1 Non-blind-watermarking
Distinguishing between
non-blind
and
blind
watermarking schemes
depending on whether or not the original digital document is required at
extraction process. The Non-blind watermarking is used for extracting the watermark
from the cover content. In other words, cover content is required during the detection
process for extracting watermark (Kumar N.M. , 2011 ). Essentially, non-blind
watermarking method extracts the watermark by comparing the watermarked picture
with the original picture.
2.7.2 Blind-watermarking
The Blind-watermarking is also used for extracting the watermark from the
cover content. Unlike Non-blind-watermarking, cover digital content is not required
during the detection process (Makhloghi, 2011). In fact in Blind watermarking
method does not need original image to extract the watermark from the watermarked
image.
2.8 Peak Signal-to-Noise Ratio (PSNR) Test
The Peak Signal-to-Noise Ratio (PSNR) value has been used to evaluate the
quality of the watermarked images and common values for PSNR can be between 30
and 40 dB. If the value of PSNR for a watermarked image is more than 30, we can
say that it is difficult to notice of the differences between cover image and
watermarked image by Human Visual System (Gil-Je Lee, 2008).
2.9
Measuring performance of watermarking
In order to obtain the purposes of digital image watermarking especially for
copyright
protection,
watermark
techniques
should
satisfy
the
following
requirements and also performance of a watermark technique will be measured by
them.
2. 9.1 Robustness
When we can say that a watermark method is robust which the watermarked
image suffers from different attacks and the embedded image (Watermark) still can
be extracted from the watermarked image (Kumar N.M. , 2011 ). In other words, the
attacks which we descried them in section 2.10, must do not have any effects in the
extraction process. For example, when a watermarked image is rotated, the algorithm
still must identify the watermark that we have embedded inside the cover image and
extract it as well.
2. 9.2 Imperceptibility
When we can say that a watermark method is imperceptible which is so hard
for human visual system (HVS) to recognize the differences between the original
image and the watermarked image (Kumar N.M. , 2011 ). This means that both
original and watermarked image must be identical and nobody can notice and find
the watermark easily.
2.9.3 Security
Security in the watermarking process plays an essential role. In order to
encoding the image by embedding secure or public key (Kumar N.M. , 2011 ) . In
fact, only the owner of the digital media must be able to extract or modify the
watermark from the digital media. Otherwise our method could be insecure and
anybody can manipulate the digital media and put their watermark as well.
2.10
Watermarking Attacks
Watermark attacks wished to ruin or remove the watermark signal form the
digital watermarked content. Hence, it is desirable to understand how those attacks
will be occurred to design a more robust watermarking method against them.
2.10.1
Classification of attacks
Watermarking attacks can be divided into the four main categories. They are
removal, geometric, protocol and cryptographic attacks (Voloshynovskiy, 2001). In
the below figure illustrates them along with some sample of common their attacks.
Figure 2.8
Watermarking Attacks (Voloshynovskiy, 2001)
2.10.1.1 Removal attacks
This type of attacks aimed to remove the watermark from the watermarked
image without using any decryption algorithm. In other words, this type does not aim
to find out the algorithm which is used for embedding watermark into the host image.
In fact, removal attacks affect in the magnitude of watermark signals and can
removal the watermark or decrease the energy of host signals. Hence, extract the
watermark from the watermarked content becomes difficult. This type of attack
includes noising, histogram, filtering, blur, and sharpen attacks.
2.10.1.2 Geometric attacks
Geometric attacks aimed to the embedded data in the watermarked image
with distort the watermark detector synchronization. In other words, they trend to
distort signals in the watermarked objects (Toshihiko Yamasaki, 2009).
Some of the common attacks in this category are image rotation, translation,
skewing, cropping, and scaling. Furthermore, there are three directions to solve such
these attacks. The first method is in geometric constant domain. The second one is
embed an extra template to evaluate geometric transformation. The last one is digital
watermarking based on the feature points (Jinguang Sun, 2010). Moreover, two new
attacks which try to destroy low ISB bits (2,3,4 bit plans) in watermarked image have
been introduced including Set Removal attack, Reset Removal attack (Mir Shahriar
Emami, 2011).
Figure 2.9 8 bit planes of a gray scale image (Mir Shahriar Emami, 2011)
Several techniques have been proposed in literature for solving this problem
so far. For example, algorithms by employing SIFT. In fact, SIFT is a theory for
extracting features. It can detect those points that are changeless to location, rotation
and scale (Jinguang Sun, 2010).
2.10.1.3 Protocol attacks
In this type of attack, attackers try to add their own watermark into the
watermarked object instead of the main watermark (Martin Kutter, 2000). It causes
that realizing the main owner of the object become difficult. For an instance, copy
attack is one of the protocol attacks which do not aim to destroy the watermark, but it
tries to find out the watermark and copy the watermark into some other objects.
Furthermore, the discovered watermark will be accustomed to the other objects and
will fulfill their imperceptibles. Some of the common attacks in this category are
invertible and copy attack.
2.10.1.4 Cryptographic attacks
The main of this attack is to find out the watermark and remove it from the
watermarked object or to put misleading watermarks (Martin Kutter, 2000). In fact,
this type of attack trends to crack the security of the watermarked object. Some of the
common attacks in this category are brute-force and Oracle attack. Furthermore, due
to their complexity, application of this type of attack is limited.
2.11
Spatial Domain Methods
As we already mentioned, there are two types of watermarking techniques
based on the domain, namely spatial and frequency domains. However, both of them
have their own benefits. One of the benefits of the spatial domain method is that they
can be applied to any images easily. Spatial watermarking is also popular due to
provide tradeoff between robustness and quality in the watermarked objects. There
are two important keys for the spatial watermarking which are the region of the host
image that we can embed the watermark and as well as the information inside the
watermark.
Furthermore, there are also three main factors which can define the
parameters of algorithms in spatial domain watermarking (Megalingam, 2010).
Firstly, watermark is closely connected to the quality and size of the watermark
image. Secondly, using a secret key in the process of watermarking can improve the
security of watermark. In term of using secret key only authorized recipient can
extract watermark. The last one is related to the masking property of the image. It is
also connected to the quality of the image that indicates the transparency of the
watermark into the host image.
2.11.1 Least Significant Bit
Least Significant Bit (LSB) is one of the first techniques for watermarking
and it uses the lowest bit plane of the bitmap images. It can modify bits of both cover
and watermark image for embedding the given watermark. The basic idea behind this
technique is substitution of the lowest bit plane of the host image with the
watermark. In other words, the spatial domain technique embeds the direct
watermark into the cover image by altering the pixel values (Gil-Je Lee, 2008).
Moreover, LSB is extremely easy in term of implementation and also eye
cannot recognize very minor digressions (Ker, 2004). However, this method is
imperceptible and there is no significant difference between watermarked image and
original image, but it is not robustness enough to protect the authority of
watermarked images. This means that in the spatial domain embedding capacity can
be large, but the watermark could be easily found by unauthorized forgers.
Figure 2.10
LSB and MSB bit-Planes
In addition, there are 8 bits for storing each pixel in a grayscale image system.
As can be seen from the figure 2.10, we have one pixel value of the cover image and
also we have the secret data in the figure, which their value are 192(011000000)2
and 1(1)2 respectively. It applies to 1bit LSB that altered the value of pixel and
changes it to 193(011000001)2. Undoubtedly, LSB is an imperceptible technique;
due to changing the least significant bit value of each pixel does not have any
significant effects in a digital media (Puneet Kr Sharma, 2012).
Figure 2.11
one pixel value of the cover image (a), one pixel value of
secret data or watermark (b), change one pixel value of cover image by a bit of
secret data (c).
Obviously, by using LSB we will be able to store 1 bit in each pixel. For
example, in an image with size of 128*128, LSB can only store 16384 bits into the
cover image.
As a result, this type of watermarking technique is not robust and forgers
easily can access to the secret data and remove it from the digital content. It also
could be more fragile, when we put the secret data in a sequence order.
2.11.2 Most Significant Bit
The main primary concept of Most Significant Bit (MSB) is the same as LSB
that we have described in the previous section. The only difference between them is
that, LSB uses the least significant bit, meanwhile in the MSB is completely different
and it uses most significant bits instead.
However, the base idea of LSB and MSB is the same, but they have big
difference in term of imperceptibility and robustness. As we discussed in the
previous section, those algorithms which use LSB are not robust. We also mentioned
that LSB has some advantages like imperceptibility, but algorithms which use MSB
are not imperceptible. Because changing the most significant bit in the pixel value of
an image has considerable effect on the image. For instance, if we have
193(011000001)2 as a pixel value of a cover image and 3 bits of our watermark will
be 4 (100)2, the result for MSB will be 129 (100000001) 2. As can be seen, the result
is very far from the cover pixel.
2.12 Frequency Domain Methods
Watermarking algorithms in frequency domain may use different transform
functions such as Discrete Sine Transform, Discrete Cosine Transform, Discrete
Wavelet Transform, and Discrete Hartley Transform which abbreviation for them are
DST, DCT, DWT, and DHT respectively (Megalingam, 2010).
In addition, in frequency domain image watermarking, both host and
watermark image must transmute into the frequency domain. Therefore, it is
extremely hard to find the watermark in the frequency domain.
(1)
Where, Vi’: is the result of added DCT of the both host and watermark image. Wi: is
DCT value of the watermark image. Xi: is DCT value of the host image.
Also, the visibility of the watermark will be adjusted by Į and ȕ as two
constants. For instance, in the above formula if we adjust ȕ as a low value,
watermark will be invisible and if we change it to a high value, watermark will be
more visible as well. Also, if we want to extract watermark, we need to know the
values of Į and ȕ, otherwise the process of extracting watermark will not be
completed. It causes that the security of the watermarked images will be increased.
Moreover, it can be more secure when the value of each block for Į and ȕ are
different and only the owner of image knows about their values.
Figure 2.12
Digital watermarking based on DCT
It the figure 2.12, we have demonstrated watermarking in frequency domain
by using Discreet Wavelet Transform. In the figure there are a logo and a Lena
image which are our watermark and host image, respectively. Watermarked image
also is provided in the above figure. However, computation of DCT is more complex
than as shown in the figure and it also involves lots of complex matrix
manipulations.
2.13 Related work
A new LSB algorithm based on the digital watermarking scheme has been
presented (Gil-Je Lee, 2008). Moreover, the proposed algorithm works in the
grayscale image and this technique also embed the same data into the cover image as
traditional LSB technique. In fact, the proposed algorithm enhances the traditional
LSB algorithm to make it more secure and robust. The authors believe that their
algorithm is robust against some attacks like cropping and equalization attacks and
attackers must get the seed for detecting the watermark.
A simple algorithm for both embedding and extracting watermark in the
spatial domain has been proposed (Megalingam, 2010). The formulas are also
provided as:
Ǔ=Y+ĮI
(2)
Where, Ǔ: is the watermarked image. Y (i, j): is the intensity of the host
image in the position of i and j. Į I: is the watermark. Moreover, by using the below
formula, the authorized owner can extract watermark by comparing the watermarked
image and the original image.
I = (Ǔ - Y) / Į
(3)
As can be seen in the figure 2.8, there are four diversified pixel intensity
matrices which called lowest
intensity
matrix(LL),
Intermediate
Intensity
Matrix(LH) ,Higher Intensity Matrix(HL) and Highest Intensity Matrix(HH). The
pixel intensity matrix of the host image will be compared with four constant
values which constants are 63,127,191 and 255 for 8-bit encoded pixel data.
Figure 2.13
pixel intensity matrixes
In fact, (Megalingam, 2010) believe that their algorithm is working different
by other conventional algorithms that have been proposed so far in spatial domain
watermarking. Also, their mentioned which their method is robustness enough and it
guarantees an average PSNR for all images. In their algorithm, all values of each
pixel will be compared with all four diversified intensity matrices and will be
classified by regarding to the table 2.1.
Table 2.1
Intensity Matrices
(Mir Shahriar Emami, 2011) proposed two new Geometric attacks including
Set Removal (SR) and Reset Removal (RR) attack which aim to replace and remove
the embedded watermarks in a watermarked media and also evaluates the accuracy
of the watermarking robustness metrics. It also uses all possible capacity of the
watermark embedding bit-plane, so the watermark will be embedded several times in
this bit-plane. In the extracting process, in order to determine the robustness, the
highest obtained value as the degree of the similarity between the original watermark
image and the attacked watermark image is considered.
(Chan, 2004) proposed a simple watermarking algorithm based on the Least
Significant Bit in grayscale image. They believe which their algorithm is more robust
than the traditional LSB technique. In fact, they proposed a new LSB algorithm
which uses the third and the fourth least significant bits of cover image content’s
value for embedding watermark inside the cover digital content. They also added that
their algorithm does not have any obvious distortion in the watermarked image.
CHAPTER 3
RESEARCH METHODOLOGY
3.1 Introduction
In this chapter, the methodologies and techniques which have been applied to
achieve the objectives of the study have been presented. In addition, the watermark
and host images that have been used as datasets for the study are introduced as well.
The approach tries to achieve the best quality, high robustness, and improving the
security of watermarked image in comparison by the LSB technique. In the rest, a
mathematic formula as a key for embedding watermark has been presented to
improve the security and a repeating method is discussed for improving the
robustness as well.
Moreover, in this chapter we are going to verify them and apply them to
making the tradeoff between robustness and security as well. At the end of current
chapter, two main phases have been discussed including embedding and extracting
phases. In the first phase watermark will be embed into the cover image and then
some attacks will be applied on them for evaluation and finally by using the
extracting phase, the watermark will be extracted from the watermarked image.
3.2 Research environment
The algorithms of this study have been implemented by MATLAB R2010b
and visual C .Furthermore, both watermark and host images have been prepared by
Adobe Photoshop cs3. The below figure (Figure 3.1) depicts watermark and host
images in size of 50*50 and 256*256, respectively.
Figure 3.1
Grayscale watermark (50*50) and host images (256*256)
As can be seen from the Figure 3.1, two standard grayscale host images have
been used, namely Lenna and Lake. The dimension of each one is 256* 256 pixels
and these images are standard images that can be found and downloaded from the
Internet.
3.3
Introduction of the new method
A new approach to discover the best watermarked image quality as well as
the best robustness is presented. In this section, a trade-off between quality and
robustness must be achieved. To reach the goal of robustness, a repeating method has
been introduced. Furthermore, by using a non-sequential mathematic formula for
embedding, security has been reached.
3.3.1 The best quality of image
There is no doubt that the quality of the watermarked image is the most
significant parameter in all invisible methods. In all invisible watermarking methods,
watermark must be embedded with having the least effect on the quality of the host
data. In order to improve the quality of the watermarked image, beside the security
an Intermediate Significant Bit (ISB) method has been used. This means that only
one bit between bit 2 and bit 7 will be changed to guarantee the quality of
watermarked image and the other 7 bits will not be changed. As can be seen from the
figure 3.1, all eight layers of both watermark and host images have been presented.
Figure 3.2 Eight layers of host and watermark images
3.3.2 The best robustness
Robustness as another concern in watermarking also must be improved. In
this section, this goal is achieved by repeating the embedded bits of watermark for
certain times. This idea tries to make the algorithm more robust against attacks.
Hence, the watermark is encoded into main signals T times. For instance, in this case
we have considered T=3 and image is divided into blocks of with size of 3 pixels
which all of the values of each block must be same as it is demonstrated in the figure
3.3.
Figure 3.3
divided cover images into blocks
To clarify, the below figure depicts how one pixel of watermark is embedded
into the cover image. If the first pixel of watermark contains (11011101), the pattern
of embedding by considering this assumption that the method of embedding is
sequential, must be:
Figure 3.4
Repeating bits in each block
By this assumption that each bit of watermark image is repeated into the
cover image three times, in embedding process, for extracting process also must be
done the reverse of this process. This procedure protects watermark against attacks
and can decrease the risk of losing bits that can be damaged by the attacks. This
process, verify the majority of bits while extracting watermark to guess that what the
value of watermark is. In other words, the algorithms read all three bits for one pixel
and make decision between them. For example, if T=3, (000) represents 0, the (101)
depicts 1, the (001) represents 0 and so on. To improve the robustness of the
watermarked image, one bit in each pixel can be embedded in a block of pixels
instead of embedding in only one pixel. This method has some advantages and
disadvantages as well. The most important disadvantage of this method is decreasing
the size of the watermark data which can be embedded in the host image. On the
other hand, this approach can make a good robustness by increasing the size of each
block. Moreover, each block can be divided into 3, 5, 7, and 9 pixels as can be seen
from the figure 3.5.
Figure 3.5
different sizes of blocks
3.3.3 Variable Repetition
To further improve robustness of watermarking besides keeping a high
capacity of watermarked image, the above mentioned method of repeating the
embedding bits, based on average of pixels value can be chosen manually. The
number of repeating might be high for the Most Significant Bits (MSB) of the
embedded image and low for the Least Significant Bits (LSB). This method may
ensure a high quality of embedding image, besides keeping the large amount
capacity.
3.4 Integrate watermarking
Beside the improvement of robustness in the watermarking system, other
parameters also need to improve such as security and capacity of image which we
can hide information inside it. Hence, a few steps need to be developed which
guarantee that the proposed method is more robust, secure as well. Security beside
the robustness is also important and may help to cut-off the attacks of malicious
users. It is important to note that all of these stages can be executed separately. In the
following section more details have been discussed.
3.5
Security in watermarking
Security is a vital parameter in the watermarking system which must be
considered. This means a strategy must be used to protect watermark against
malicious users and also this strategy must be hidden. In other words, the watermark
must not be able to detect by the people who do not right to modify or manipulate the
watermarked image. In addition, the security in watermarking is represented same as
security techniques which have been used in encryption methods (Seitz, 2005).
The security of watermarking needs a separate research effort. Nevertheless,
in this study a method for embedding bits of watermark has been presented to
enhance the security of watermarked images. This method is using the encryption
method to improve the security of watermark. As an example, Data Encryption
Standard (DES) is using the shared key to encrypt the message. This technique might
use to make more additional security to the watermarked image. The “Random Pixel
Manipulation” is another example of encryption (Venkatraman, 2004). The idea
behind this method is using the random positions of data instead of sequential
positions to overcome the security problems. In the below section this method has
described in more detail.
3.5.1 Random Pixel Manipulation methods
The Random Pixel Manipulation technique uses a key that can specify the
sequence of bits. This key provides an integer number to help generating a repeated
sequence of unique numbers which its rage starts form 1 to N; where N is the number
of current pixels. One of the advantages of this method is that the pixels are
distributed to the whole image and decrease the distortion to the host image.
Figure 3.6 Hashing bits into the host image (M. Zeki, 2011)
As can be seen from the figure 3.6, it is clear that all the bits in watermark
can be embedded thorough the host image to make it both robust and secure. This
technique is quiet secure, but the problem is that we need to keep the places of bits in
an extra place such as table or file. Regardless of this problem, this method is the
best way to secure images even it can be executed with the repeated bits which have
been described in section 3.3.2. Hence, if an area of watermarked image will be
destroyed, the algorithm can use the other two bits from the other parts of image to
extract the watermark in extracting process.
3.5.2 Zig-zag embedding matrix
To improve the security of watermarked image, a non-sequential algorithm is
needed. A Zig-zag array is a square collection of the first N^2 integers and the
numbers increase sequentially as can be seen from the figure 3.7. Hence, the
watermark pixels are not sequential and watermark cannot easily extract by
unauthorized users (M.Padmaa, 2010).
Figure 3.7 Zig-zag embedding matrix (M.Padmaa, 2010)
The main aim of using Zig-Zag matrix is to improve the security of
watermarking, besides using a repeating method to increase robustness. Hence, each
pixel of watermark will be repeated three times within cover image by using this
embedding algorithm to improve both security and robustness.
3.6 Enhancement of capacity in watermarked image by Pixel value Differencing
(PVD) method
The word of capacity in watermarking deals with the amount of data which
can be embedded inside the host image. The main aim of capacity is to embed data as
much as possible into the host image, and this goal should be achieved beside the
other parameters like robustness and security which have been discussed in previous
sections.
In the embedding process, the host image must be divided into blocks, for
example 3*3. This process also uses a key for embedding. Then, the watermark must
be converted into the stream of binary bits and each bit must be embedded within the
block of host image. Moreover, the number of bits which can be embedded in to each
block is different and it depends on the each block and their values. Hence, each
pixel might be different by another pixel. The sequence of watermarked data must be
selected wisely and inherited by key which we can use this key for recovering the
watermark later in extracting process. In the figure 3.8 the process of this technique
has been illustrated as well.
Figure 3.8
Capacity improvement techniques
3.7 Evaluation of Quality and Robustness
To evaluate the robustness of the proposed algorithm we have used the
Normalized Cross Correlation (NCC) .NCC is an important performance factor in
any extracting component (Latha Parameswaran, 2006).
(3.1)
Where, W’(x,y) is the extracted watermark and W(x,y) is the watermark
image. The PSNR greater or equal than 30 db (PSNR>=30 db) is the highest NCC
and the algorithm will keep the position which is considered the best place for
embedding watermark.
Furthermore, to evaluate the quality of watermarked image we have used the
Peak Signal to Noise Ratio (PSNR). Generally, a processed image is acceptable to
the human eyes if its PSNR is greater than 30 dB (Chen, 2003). The greater PSNR
has better image quality. The PSNR is defined as given by (2).
Where m and n are the size of the host and watermarked images, ȕij is the
pixel of the watermarked image and Įij is the pixel of the host image.
3.8 Embedding process
In this phase, the watermark image must be embedded into the host image by
using the techniques which have been described in previous sections. As can be seen
from the previous sections, the methods which described try to improve the
robustness and imperceptibility. Figure 3.9, indicates the process of embedding the
watermark. The process will be started by converting watermark from pixel to bits.
Hence, for each pixel there are 8 bits which each bit will embed by using a Zig-zag
algorithm within different bit-planes in cover image. This process will be continued
until all of the bits in watermark have been embedded.
3.9 Embedding Phase
Figure 3.9
Embedding watermark phase’s framework
3.10 Extracting Phase
In this phase, the watermark will be extracted from the watermarked image.
This phase uses the reverse algorithms which have been used to embedding bits
through the host image. The process has been shown on the below figure.
Figure 3.10
extracting watermark phase’s framework
3.11 Applying attacks
In this section, we will apply some attacks into the watermarked image and
then quality and robustness of image will be evaluated.
Figure 3.11
Applying attack phase’s framework
3.12 Dissertation's Framework
Start
Read the watermark image
Extract stream bits of watermark image
Find the best place for putting
watermark into the host image
Select the best layer to put the bits of
watermark between 2 and 7
Repeating one bit of watermark tree
times within the image
Y
Satisfied
condition
s?
N
Evaluate PSNR
and NCC of
watermarked image
Read the watermarked image
Watermarked
Image
Convert watermarked image to a string
of bits
Apply attacks such as
Sharpening Filter, adding
salt and pepper noise,
Blurring
Extract three bits of each pixel of
watermark (T1, T2, and T3)
Compare 3 bits together and make a
decision between them to find of the
exact value of the watermark’s bit. If
two or three bits are 0 then value is 0,
else the value is 1
Put the bits into the watermark
buffer and make the watermark
End
Figure 3.12
Dissertation Framework
3.13 Summary
In this chapter, some methods for embedding watermark into the host image
have been proposed such as variable repatriation, Random Pixel Manipulation and
etc. The proposed methods will be implemented in two main phases which are
embedding and extracting watermark and they aim to reach the main objectives of
this dissertation. The proposed algorithm in this methodology will be solved the
problems in watermarking area like: imperceptibility, quality and robustness. The
proposed algorithm uses Intermediate Significant Bits (ISB) as the main method for
embedding information. At the end, we will evaluate our selected bits by using
PSNR, MSE, and NCC to guarantee the quality and robustness of watermarked
image, respectively.
CHAPTER 4
IMPLEMENTATION AND EXPERIMENTAL RESULTS
4.1 Introduction
In the previous chapter, we have theoretically verified the methods which
have been used to implement the proposed technique. In the current chapter, we want
to explain it more and the actual embedding into some standard host images have
been implemented and the results are also provided. Moreover, a discussion to find
the best bit plane of image in spatial domain has been presented as well.
First of all, the host image is divided into the different sizes of blocks to
enhance the robustness as the main parameter of watermarking. Then, the watermark
is embedded into the different blocks to make it robust. As we explained in the
previous chapter, each bit of watermark is repeated three times. Next, the place of
each bit of watermark will be chosen by using the embedding algorithm through the
host image. Next, the watermarked image will be created. Finally, some attacks will
be applied into the watermarked image and the process of extracting tries to extract
watermark as well as possible.
4.2 The chosen attacks
First of all, some different attacks which might be happen for the images and
make effects on them have been provided in the Figure 4.1. The attacks include:
Gaussian filter, Speckle noise, Salt and Pepper, Passion, Set Removal (SR), Reset
Removal (RR), and Blurring. Also, the comparison between them which includes the
average percentage of effect per pixel has been displayed in the Table 4.1.
Figure 4.1
Original Host image and different attacks
The percentage of effect per pixel has been calculated by the blow
mathematic formula. Where N is the number of pixels, P is the original pixel and P’
is the value of pixel after applying the attacks. As can be seen from the table 4.1,
blurring filter has the most significant effect on the images, and the Gaussian filter
has the least significant effect on the images.
(4.1)
Table 4.1 the impact of attacks on Images per pixel
Attacks
Average percentage of attacks’ effects
Gaussian Filter
1.5735
Speckle Noise
5.9014
Salt and pepper
2.2041
Poisson
4.2337
Blurring
7.3540
4.3 The implementation
In this section, the implementation about the methodology which has been
discussed in the previous chapter has been done and the result also provided at the
end of this chapter. The implementation tries to improve the robustness, the quality,
and the security of current image watermarking methods. For achieving these goals,
a repeating method has been proposed for the robustness and security as well. For
reaching the goal of quality in the watermarked images an ISB method has been
proposed.
4.4
Evaluation of watermarking methods
There are several mathematic equations which have been using to qualify the
watermarking algorithm, examining tests on the resulted watermarked image. They
can evaluate the quality of watermarked image by comparing the watermarked image
with the original cover image. In the rest, some of the equations for evaluating the
watermark have been presented.
4.4.1 Mean Squared Error (MSE)
One of the basic tests that were executed to test whether two images are
similar is Mean Squared Error (MSE). This function could be easily written
according to equation 4.2 and gets two images for measuring the average of the
squares of the errors. The error is the amount by which the value specifies by the
estimator differs from the quantity to be estimated.
"# $ "%# & !
'(
4.4.2 Pick Signal to Noise Ratio (PSNR)
Pick Signal to Noise Ratio (PSNR) is commonly used as a measure of quality
of renovation for image compression and has a better evaluation than MSE, since it
also evaluate the signal strength and error together. The equation 4.3 shows how this
value is obtained.
2345
8
6*7
)*+, - ./0 1
(4.3)
4.5 watermarking with the best quality
The quality in digital image watermarking plays a vital role and this
parameter must be achieved during embedding process. In the embedding process
there are eight options to put a bit of watermark in the host image. The most
important thing is which bit planes (or layers) of host image is the most suitable and
has the least effect in the quality of watermarked image. The table 4.2 has been
demonstrated embedding image into all eight bit planes from Least Significant Bit
(LSB) until Most Significant Bit (MSB). The table also provides the information
about the quality of each watermarked image by using PSNR and MSE.
4.6 watermarking with improve security
In this section, the approach which has been used to improve security has
been discussed and implemented as well. The main idea is that information about the
secret key leaks from the observations, for example, watermarked parts of content,
available to the opponent.
The security of watermarked image is achieved by using a non-sequential
embedding algorithm. At the first, watermark will be converted into a stream of bits
and then these bits are embedded by using Zig-zag algorithm in cover image.
Furthermore, each bit of watermark will be embedded in each pixel of cover image
(between bit-plane 2 and 7).
The figure 4.2 depicts the process of embedding watermark. The simple LSB
method is using sequential embedding method which watermark can be easily
extracted by forgers and replace with another watermark. It can change the authority
of the owner of watermarked image. This technique causes that security improve and
also each bit of watermark will be embedded by using Zig-Zag algorithm in different
bit-planes of cover image. So, if the attacker wants to recover watermark by using
this technique, it will not work and still is secure.
Figure 4.2
improving security by using Zig-zag algorithm
By using two early mentioned techniques for improving robustness and
security, we have come up by some results that have been provided in the next
section. The result compares the proposed method by LSB method for two host
images. The results also calculated PSNR and NCC for each bit planes in proposed
and LSB method.
Figure 4.3
applying Zig-zag algorithms to embed watermark
4.7 implementation and result
As mentioned before, PSNR is used to evaluate quality of image and NCC is
used to assess imperceptibility of watermarked image. The input for these functions
are two images, including cover image and watermarked image then the function
return a number to show the percentage accuracy of watermarked image. In the next
section, the result for proposed method has been provided and also it is compared
with LSB and MSB techniques.
Table 4.2: watermarked images by using proposed method for each bit plane and
their PSNR value (Lena).
Bit
Plane Watermarked Image
s
PSNR
Bit
Plan
es
1,2,3
45.7101
3,4,5
33.2965
1,3,4
39.8263
3,5,6
31.7147
2,3,4
39.6664
4,5,6
31.3294
2,4,5
33.4402
5,6,7
30.1412
Watermarked Image
PSNR
Table 4.3:
Watermarked images by using proposed method for each bit plane
and their PSNR value (Lake).
Bit
Plane Watermarked Image
s
1,2,3
PSNR
Bit
Plan
es
45.6327
3,4,5
1,3,4
31.6079
39.9086
4,5,6
31.2706
34.7739
34.5692
3,5,6
2,4,5
PSNR
40.0745
2,3,4
Watermarked Image
5,6,7
29.3477
Table 4.4:
Bit
Plan
e
The watermarked images by using LSB until MSB for each bit plane
and their PSNR value (Lena).
PSNR
Bit
Plan
e
1
59.0652
5
34.5335
2
52.9430
6
35.1333
3
46.9393
7
35.0785
4
40.8651
8
33.7072
Watermarked Image
Watermarked Image
PSNR
Table 4.5:
Bit
Plane
Watermarked images by using LSB until MSB for each bit plane and
their PSNR value (Lake).
PSNR
Bit
Plane
1
59.0291
5
36.141
7
2
52.9543
6
33.917
9
3
46.9160
7
33.178
2
4
41.1983
8
34.290
9
Watermarked Image
Watermarked Image
PSNR
As can be seen from the above results, the proposed method by using
repeating bits in three different Bit-planes (1, 2 and 3) has the least effect on the
cover image with PSNR over 45; meanwhile the worst bit planes are 5, 6 and 7 with
PSNR under 30. Nevertheless, the LSB method by using the first bit has the best
PSNR with around 60, but LSB is so fragile and the watermark can be extracted
easily by forgers. The proposed method uses different bit planes and uses a Zigzag
embedding algorithm to implant watermark onto the cover image.
In this part, the chosen attacks have been applied on the watermarked images
and the NCC has been calculated for both proposed method and LSB in tables 4.6,
4.7, 4.8, respectively.
Table 4.6:
NCC value of proposed method
BitPlanes
Blurring
Speckle
Gaussian
Salt and pepper
1,2,3
0.7536
0.7680
0.7221
0.7966
1,3,4
0.6535
0.6876
0.6249
0.7954
2,3,4
0.7537
0.7503
0.7444
0.7970
2,4,5
0.7228
0.71.88
0.7257
0.7942
3,4,5
0.6532
0.6105
0.6488
0.7944
3,5,6
0.6359
0.5855
0.6097
0.8073
4,5,6
0.6056
0.5595
0.5761
0.8068
5,6,7
0.5939
0.5470
0.5536
0.8499
Table 4.7:
NCC Value for LSB until MSB under different attacks (Lena)
BitPlane
Blurring
Speckle
Gaussian
Salt and pepper
1
0.7809
0.7804
0.7820
0.7853
2
0.7803
0.7800
0.7820
0.7869
3
0.7789
0.7781
0.7820
0.7857
4
0.7760
0.7748
0.7820
0.7815
5
0.7759
0.7740
0.7820
0.7810
6
0.7383
0.7346
0.7820
0.7467
7
0.7367
0.7298
0.7820
0.7421
8
0.8352
0.8167
0.7820
0.8386
Table 4.8:
NCC value for LSB until MSB under different attacks (Lake)
BitPlane
Blurring
Speckle
Gaussian
Salt and pepper
1
0.9520
0.9919
0.7820
0.9935
2
0.9514
0.9948
0.7820
0.9943
3
0.9503
0.9923
0.7820
0.9919
4
0.9453
0.9881
0.7820
0.9900
5
0.9348
0.9788
0.7820
0.9778
6
0.9641
0.9994
0.7820
1
7
0.9893
1
0.7820
1
8
0.7124
0.7130
0.7820
0.7289
The extracted watermark‘s result for proposed method has been done and
illustrated in the tables 4.8 and 4.9. The attacks that have been chosen, described in
previous chapter s and the amount of their effects has been written at the first of this
chapter.
Table 4.9:
Bitplanes
Extracted watermark after attack in proposed method (Lena)
Salt and
Pepper
Gaussian
Speckle
Poisson
Blurring
57.8942
35.1767
35.1767
35.1211
35.1767
33.1767
35.1767
31.9604
30.2204
58.0448
29.2292
28.8208
29.1933
28.9032
54.8024
29.1511
29.0914
29.2553
28.7651
57.1409
29.0285
29.7285
28.9973
28.2251
55.2978
28.9489
29.7255
29.9973
29.0686
55.2978
28.8878
31.5728
30.5728
28.9786
58.4180
29.4963
35.1767
31.9604
28.6906
1,2,3
PSNR
1,3,4
PSNR
58.0278
2,3,4
PSNR
2,4,5
PSNR
3,4,5
PSNR
3,5,6
PSNR
4,5,6
PSNR
5,6,7
PSNR
Table 4.10:
BitPlanes
Extracted watermark after applying attacks in proposed method
(Lake)
Salt and
Pepper
Gaussian
Speckle
Poisson
Blurring
58.0448
31.1767
34.1767
32.1211
30.1767
58.0448
30.1767
31.9604
31.2204
58.0444
29.2292
29.5179
29.1933
28.9032
54.8011
29.1511
30.5596
29.2553
28.7651
56.1412
29.0285
31.1895
28.9973
28.2251
55.2978
28.9417
32.2277
29.9973
29.0686
55.2978
28.8878
32.0976
30.8828
28.7786
58.4180
29.4963
36.5238
31.9604
28.6906
1,2,3
PSNR
1,3,4
PSNR
30.4285
2,3,4
PSNR
2,4,5
PSNR
3,4,5
PSNR
3,5,6
PSNR
4,5,6
PSNR
5,6,7
PSNR
Table 4.11:
BitPlanes
Extracted watermark after applying the attacks in LSB method
Salt and
Pepper
Gaussian
41.3402
27.2872
40.9581
27.4718
42.6408
Speckle
Poisson
Blurring
27.2553
27.3459
27.5265
27.3376
27.4557
27.2759
27.3816
27.3793
27.2623
41.3980
27.3839
27.5088
27.4279
27.7070
41.3206
27.4665
29.5288
27.8759
27.8265
41.6746
27.6354
32.2871
29.7421
28.2866
41.8143
29.4687
34.7684
32.8219
27.4516
40.7806
32.2926
39.5445
36.0818
27.4319
1
PSNR
27.4521
2
PSNR
3
PSNR
4
PSNR
5
PSNR
6
PSNR
7
PSNR
8
PSNR
As can be inferred from the tables 4.9, 4.10, and 4.11, the proposed method is
more robust in comparison by LSB and MSB methods. Nevertheless, most of the
attacks tend to destroy low bits (1, 2, and 3), but the proposed method can extract
watermark after the attacks by acceptable percentage of healthy pixels in watermark
even in low bit-planes. Surprisingly, as can be seen from the result of proposed
method, the average PSNR in all of the 8 layers is approximately same by over 30
db. Hence, above result proves that watermark can embed onto the all bit-planes of
cover image regardless of the visibility of watermark. The below figures also
provided information about the PSNR of watermark after extraction for two LSB and
proposed method. The x-axis indicated 8 bit-planes and the PSNR is indicated on yaxis.
Figure 4.4:
Applied salt and pepper attack for both proposed and LSB method
Figure 4.5
Figure 4.6
Applied Gaussian attack for both proposed and LSB method
applied Speckle attack for both proposed and LSB method
Figure 4.7
Applied Poisson attack for both proposed and LSB method
Figure 4.8
Applied Blurring attack for both proposed and LSB method
4.8 Improving the capacity
The capacity is also another important parameter of watermarking and it is
needed to be increased. Obviously, the best method of watermarking in term of
capacity is embedding more information into the host image. The capacity of the
watermark method can be simply evaluated by increasing the length of the
watermarking message. Furthermore, any watermarking method is not capable of
holding more than a definite length of data or it might endanger its imperceptibility.
4.9 Summary
In this chapter, the quality and robustness of watermarked image has been
found to enhance by using a Zig-zag algorithm and repeating method in different bitplanes. Zig-zag algorithm tries to embed watermark in a non-sequential way into the
cover image, and repeating method helps to embed watermark in different places and
different bit-planes of cover image. Therefore, it causes that if a bit-plane has been
destroyed by mentioned attacks, uses two other bit-planes and makes decision
between them by using the majority of bit values. The experimental results also
provided in this chapter that specify the proposed method has many advantages
compared to other spatial methods such as LSB and MSB.
The host image was portioned into block size of 3 pixels and proposed
method was also applied to these blocks. The result also shows that the NCC and
PSNR value were improved in the embedding number for all attacks and all
mentioned attacks has approximately the same effect in all bit-planes at over 30db.
The proposed method aims to embed and extract watermark pixels in
grayscale, but it could be black and white image. Hence, each pixel in watermark can
be embedded in each pixel of the host image. Therefore, regardless of the repeating
method for improving the robustness, the space for the watermark could be the same
as host image. For example, if we have a cover image with size of 256*256, we can
embed a watermark the same is cover image size.
CHAPTER 5
CONCLUSION
5.1 Introduction
Over the last half century the pace of change in term of types of documents
and methods of transferring data from one part to another part has been increased
where billions of bits of data is created in every fraction of a second and transfer
through the Internet rapidly. Hence, digital watermarking came as a method and a
tool to overcome copyright concerns for digital documents.
Watermarking is a method that is use to hide information or identify data
within digital documents. Moreover, digital documents can be divided into video,
audio and image. In this dissertation, we have focused primarily on the watermarking
of digital images. Digital watermarking is becoming popular, mainly for embedding
undetectable marks, such as owner or copyright information.
5.2 Contribution and Achievement
In this study, gray scale images have been applied to embed an evidence of
the owner of the digital image. Digital images can be divided into two domains
including frequency and spatial domains and the proposed method implemented in
spatial domain to embed watermark within cover image. The proposed method also
used Intermediate Significant Bits (ISB) to improve the security and quality of
watermarked image.
At the same time, some attacks also applied to the watermarked image due to
by attacking watermarked image it become very complex to recover watermark back
from the watermarked image and even if it extracted one may no longer use it to
prove the ownership and copyrights. Therefore, one of the goals was to find such
regions in the cover image which are very resistant and stable against attacks.
In this study, we have proposed a new watermarking method to improve
previous works in spatial domain in terms of robustness and imperceptibility.
Moreover, we have discussed about the previous studies in the literature. Also, some
of the remained problems in spatial domain have been discussed such as security,
imperceptibility, quality, capacity, and robustness. The proposed method has been
aimed to improve robustness of watermarked image by using repeated variable for
each pixel of watermark and it improves the probability of extracting intact bits after
attacks and the security guaranteed by using a secret key and none-sequential
embedding method (Zig-zag).
The experimental result for the current dissertation has been recognized based
on the literature review which has been discussed in chapter two about spatial
domain methods such as LSB and ISB techniques. However, the experiments in
chapter four have been proved that the problems that occur in the spatial domain
have been solved and the result compared with other recent techniques. Therefore,
the achievements of the dissertation are summarizes as follow:
a. Improve the problem of quality and imperceptibility of watermarked image
by using an ISB method in spatial domain. For this goal, intermediate bits
have been chosen to be imperceptible enough and no one can detect the
existence of watermark easily.
b.
Improve the robustness of watermarked image by using a repeating method
for each pixel of watermark within cover image. Using an reparative
technique in different bit planes helps to improve watermarking robustness.
In other words, if a watermarked image has been attacked between sender
and receiver or during its life time, it still can extract and can use other bits of
repeated pixel within the watermarked image.
c. Improve the security of watermarked by distributing pixel throughout the
cover image (none-sequential method). Obviously, using a none-sequential
embedding algorithm within cover image helps to improve the security of
image against forgers. In other words, we have used a secret key for
embedding and then it will be used in extracting process.
5.3 Future Work
Although the proposed method is tried to achieve the best quality, robustness,
and security for watermarking in grayscale images, but some other important
parameters must be considered as well. In the following some of the remained
problems have been listed as:
1.
The most important remained parameter in watermarking is capacity. In
this study, a repeated method for improving robustness has been used,
meanwhile using this technique causes that the amount of watermark
information decreased due to watermark must be repeated T times into the
cover image. Nevertheless, some researchers have been tried to increase
the amount of information into the cover image, but it still needs to
improve.
2.
The Integrity is also another important concern in digital image
watermarking. In other words, when a watermark is embedded within the
cover image, might be extracted and embedded with a fake watermark.
Hence, the ownership of cover image will endanger about this type of
attack. Therefore, the methods need to overcome these kinds of problems.
3.
Although, grayscale image for this study were used, but the present study
also can extend for colored image (RGB).
5.4 Summary
The general idea of this dissertation is to improve the imperceptibility and
robustness of digital image watermarking by proposing new intermediate significant
bits method and reparative watermark bits within different bit planes of watermarked
image, respectively. The security also in the dissertation has been improved, due to
using none-sequential embedding process and using a secret key to embedding and
extracting watermark. In order to evaluate the performance of proposed method,
several experiments have been conducted. Finally, the proposed method has been
compared with conventional method and also the experiment results did reveal that
the proposed method is capable to solve the problems related to imperceptibility and
robustness.
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