Vandana Yadav
1
, Dr. Parvinder Singh
2
Vandana1055@gmail.com, Parvinder23@rediffmail.com
Department of CSE, Deenbandhu Chhotu Ram University of Science and Technology, Murthal,
Sonipat, Haryana-131039
Abstract - Due to the rapid development in computer networks , digital data can easily be copied, modified and distributed to an illegal way. So the copy right protection, intellectual protection, material protection of owner, buyer and distribute is necessary. The solution of this problem is watermarking. A watermark is an imperceptible, robust, and secure message embedded directly in digital element such as image. In image watermarking, information is embedded into cover media using spatial domain or frequency domain technique. This paper elaborates suitability of wavelet transform for image watermarking, wavelet transform based image watermarking process, and provide a review of the existing literature available on wavelet based digital image watermarking and wavelets and comparison of different wavelets available.
Keywords - Digital Image Watermarking, Discrete Wavelet Transform (DWT), Embedding,
Extraction.
I. Introduction
With the unrestricted widespread use of Internet and rapid development in multimedia technology, it has become a common practice to create copy, transmit and distribute digital data and therefore it may leads to unauthorized replication problem. Therefore digital media require security of data communication over internet. There are various approaches, which provide security such as, cryptography, steganography and watermarking. In Watermarking, a watermark is inserted in the digital media like image. Digital Image Watermarking provides copyright protection to image by hiding appropriate information in original image to declare rightful ownership [1]. Digital watermarking is a technique in which a piece of digital information is embedded into an image and extracted later for ownership. There are number of requirements that are to be possessed by watermarking algorithms, these are [1],[4]:
Transparency: Watermark should be transparent to end user and should not degrade the performance of original image.
Security: Watermark information shall only be accessible to the authorized parties. Only authorized parties shall be able to alter the watermark content.
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Robustness: Watermark should be robust means that it should be difficult to remove or destroy and robust enough to withstand all kind of attacks whether intentional or not.
Imperceptible: When watermark is embedded quality of host image should not be effected by the presence of watermark.
II. Watermarking Principle
Watermarking system consists of two categories, embedding and extraction. In embedding watermark data is embedded into host image and in extraction watermark data is extracted from the image.
Host image
Watermark embedding algorithm
Watermark image
Watermark extraction algorithm
Host image
Watermark
Watermark
Fig.1 Watermarking Process
Watermark is embedding into the host image using watermark algorithm and a watermarked image is obtained at sender side and this watermark image is send to the receiver through transmission channel where noise effect the watermark object. Receiver receive the watermarked image and by applying the watermark extraction algorithm, watermark is extracted from the host image.
III. Attacks on Digital Image Watermarking
There are various possible malicious intentional or unintentional attacks and the aim of these attacks is to prevent the watermark from performing intended purpose.[3] [7]
Removal attack ; purpose is to remove the watermark data from the watermark object.
Interference attack ; add additional noise to watermark object.
Geometric attack ; manipulate the geometry of the image such as flipping, rotationa, cropping etc.
Forgery attack ; result in object insertion and deletion.
Security attack ; perform modification to render the watermark invalid or to estimate and modify the watermark. In this case we talk about an attack on security. The watermark algorithm is considered secure if the embedded information cannot be destroyed, detected, or forged.
Cryptographic attack ; deals with the cracking of the security, for example finding the secrete watermarking key.
2

Image degradation attack ; damage robust watermark by removing parts of image. The parts that replace may carry watermark information. Example of these operation are partial cropping, row removal, column removal, insert noise.
IV. Image Watermarking Techniques
Digital watermarking techniques refers to the process of embedding and extraction of the given watermark information.
There are three stages in watermarking, there are
Generation and Embedding: Generate the watermark and embed in the original image
Distribution and Possible attacks: Send the watermark image. Possible attacks in the broadcast channel may be intentional or not.
Detection: detect the owner and provide the information to intended receiver [18].
Image watermarking techniques divides into two domains; special domain and Frequency domain.
Spatial domain
In spatial domain the modification would be on the pixels values directly specifically the least significant bit LSB, because it hasn't great effect on the overall pixel value but this technique although it's simple and has less complexity time but it's not robust enough to resist attacks and watermark can easily be detected by intruders.
Frequency domain
There are various frequency domain techniques; Discrete Fourier Transform (DFT), Discrete
Cosine Transform (DCT), Discrete Wavelet Transform (DWT).
Discrete Fourier Transform
Fourier transform is a operation that transform a continuous function into its frequency components. The equivalent transform for discrete valued function requires the Discrete Fourier
Transform (DFT). In digital image processing, the even function that is not periodic can be expressed as the integral of sine and/or cosine multiplied by a weighting function. This weighting function makes up the coefficients of Fourier Transform of the signal. Fourier
Transform allows analysis and processing of the signal in its frequency domain by means of analyzing and modifying these coefficients.
3

Discrete Cosine Transform
Discrete Cosine Transform is related to DFT in a sense that it transforms a time domain signal into its frequency components. The DCT use only the real part of the DFT coefficients. DCT has a strong energy compaction property and most of the signal information tends to be concentrated in a few low-frequency component of the DCT.
Discrete wavelet Transform
The disadvantage of Fourier theory is that it has only frequency resolution and no time resolution. This means that although we might be able to determine all the frequencies present in a signal we do not know when they are present so the solution of this is Wavelet. Wavelet
Transform has both frequency and time resolution. Wavelet is a mathematical function that cut up data into different frequency components.
Table below shows the comparison of different Watermarking techniques [30], [31], [33]
Sr. no Embedding
LSB
DCT
DWT
At least significant bit.
Divide image into frequency band and embed watermark in frequency band
Divide the image into LL,
LH, HL, HH frequency bands and embed water mark in frequency band.
Attacks
Blurring
Scaled to half size
Resizing of scaled image
Cropping
Blurring
Cropping
Scaled to half size
Resizing of scaled image
Blurring
Cropping
Scaled to half size
Resizing of scaled image
Performance
Easy
Take less time high compression ratio, small bit error rate more accurate, robust, Multi-resolution characteristic, Provide better quality, noise reduction, HVS
Table 1. Different watermarking techniques.
V. Discrete Wavelet Transform
Discrete Wavelet Transform has both frequency and time resolution. The DWT separates an image into a lower resolution approximation image (LL) as well horizontal (HL), vertical (LH) and diagonal (HH). The LL band stands for the coarse one which represents the low frequency part where most energy focuses. The sub-bands labeled HL, LH, and HH represent the details of wavelet coefficients. To obtain the next coarser wavelet coefficients, the sub-band LL is further decomposed as shown in Figure 2. This process can be repeated several times, which is determined by the requirement of user.
4

LL
2
HL
2
HL
1
LH
2
HH
2
LH
1
HH
1
Fig.2 Level 2 DWT
1. Wavelet Transform is computationally efficient and can be implemented by using simple filter convolution.
2. With multi-resolution analysis, image can be represented at more than one resolution level.
Wavelets allow image to be described in terms of coarse overall shape and details ranging from broad to narrow.
3. Magnitude of DWT coefficients is larger in the lowest bands (LL) at each level of decomposition and is smaller for other bands (HH, LH, HL).
4. The larger the magnitude of wavelet coefficient, the more significant it is.
5. Watermark detection at lower resolutions is computationally effective because at every successive resolution level, less no. of frequency bands are involved.
6. High resolution sub bands help to easily locate edge and textures patterns in an image. [7],
[19]
Digital Image Water Marking Process Using DWT
Embedding Watermark: DWT divides the image into different levels or sub bands and watermark is embedded in DWT coefficients.
Original image
Watermark
DWT
Resize image according to LL
Embedded
Watermark in LL sub-band
IDWT of
Watermarked
Image
Watermark
Original image
Extract Watermark DWT
Fig. 3 Digital image watermark embedding and extraction using DWT [30].
Transmission : Watermarked data is passed through transmission channels, where various noise effect the watermarked data.
Extraction and Detection: Extraction is the process of extracted/obtaining the watermark from original image and Detection is the process of identifying the identity of owner.
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VI. Review of Wavelet Based Image Watermarking
B.L. Gunjal and R.R. Manthalkar[1], proposed a Discrete Wavelet Tranform based robust watermarking scheme for information hiding in digital image. In this paper, they used Arnold transform and purposed embedding and extraction algorithm and calculate PSNR for image with size MxN as
Where f(i,j) is pixel gray value of original image, f’(i,j) is pixel gray value of watermark image, max1 is the maximum pixel value of image which is equal to 255 for grayscale image pixel are represented with 8 bits.
Chih-Chin Lai and Ching-Chin Tsai[2], proposed Digital Image Watermarking Using Discrete
Wavelet Transform and Singular Value Decomposition. A hybrid image-watermarking technique based on DWT and SVD has been presented, where the watermark is embedded on the singular values of the cover image’s DWT sub band. The main objective of developing this technique is to satisfy both imperceptibility and robustness.
Bhupender Ram [3], described a Digital Image Watermarking Technique Using Discrete
Wavelet Transform and Discrete Cosine Transform, where the method operates in the frequency domain embedding a pseudo-random sequence of real number in a selected set of DCT coefficient and watermark is added in the selected coefficients with significant image energy in the discrete wavelet transform domain to ensure non-erasability of the watermark.
Sasmita Mishra et.al. [4], described a survey on digital watermarking techniques, the idea behind this survey is to study different kind of watermarking techniques and present a robust watermark data using DWT and introduce fragile and semi-fragile watermarking techniques.
Evelyn Brannock et.al. [5], described watermarking with wavelet algorithm and measure the success of algorithm by using Haar wavelet, the PSNR for each wavelet family and image is obtained, and described various requirements of Watermarking. The quality of an NxM host image (f(x,y)) is compared to the image containing the watermark (g(x,y)) using the formula,
Aree Ali Mohammed & Haval Mohammed Sidqi [8], proposed an image watermarking scheme based on multi bands wavelet transformation method. At first, the proposed scheme is tested on the spatial domain (for both a non and semi blind techniques) in order to compare its results with a frequency domain. In the frequency domain, an adaptive scheme is designed and implemented
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based on the bands selection criteria to embed the watermark. These criteria depend on the number of wavelet passes. In this work three methods are developed to embed the watermark
(one band(LL|HH|HL|LH), two bands (LL&HH | LL&HL | LL&LH | HL&LH | HL&HH |
LH&HH) and three bands (LL&HL&LH | LL&HH&HL | LL&HH&LH | LH&HH&HL) selection. This is advantage of the scheme is the involvement of a large number of wavelet bands in the embedding process.
P. Surekha and S. Sumathi [9], proposed a discrete wavelet transformed based image watermarking algorithm by using genetic algorithm. The amplification factor of the watermark is the significant parameter that helps in improving the perceptual transparency and robustness against attacks. The tradeoff between the transparency and robustness is considered as an optimization problem and is solved by applying Genetic Algorithm.
Kamran Hameed, Adeel Mumtaz, and S.A.M. Gilani[12], this paper gives an overview of various wavelet based digital watermarking techniques that have been developed for copyright protection. The author proposes a new joo’s watermarking techniques and embed watermark into wavelet DC components.
Hanaa A. Abdallah, Mohiy M. Hadhoud ,Abdalhameed A. Shaalan and Fathi E. Abd Elsamie[35], this paper proposed a wavelet based watermarking and uses blind wavelet based watermarking. In this watermark is inserted in the coarsest scale wavelet coefficiects. Wavelet divides the image into 3-levels and watermark is embed into the coarsesr scale. This scheme depends on the quantization of certain wavelet coefficients within certain amplitude ranges in a binary manner to embed meaningful information in the image.
Anuradha, Rudresh Pratap Singh[36], proposed a discrete wavelet transform based watermarking and uses haar wavelet for copy right protection, intellectual protection and material protection.
This paper introduced a suitable method of watermarking and divide the image into 3-levels using DWT.
Liang Zongbao and Wang Jiawei[37], proposed a watermarking algorithm using 3-level wavelet transform where watermark is embedded in medium frequency component on horizontal and vertical direction based on the wavelet coefficients.
Sr.
No.
Author’s name
Title Year Pag e
Description Technique used
1 B.L. Gunjal and R.R.
Manthalkar
Discrete
Wavelet
Transform based Strongly
Robust
Watermarking
Schemes for
2010-
12 no.
-
124
129
Proposed strongly a Discrete
Wavelet Tranform based robust
Watermarking scheme for
Information Hiding in digital image & used
Arnold transform for
DWT
Arnold
Transform
&
7

2
3
4
Chih-Chin
Lai
Ching-Chin
Tsai
Bhupender
Ram
Sasmita
Mishra et.al. and
Information
Hiding in
Digital Images
Digital Image
Watermarking
Using Discrete
Wavelet
Transform and
Singular Value
Decomposition
2010 306
0-
306
3
Digital Image
Watermarking
Technique
Using Discrete
Wavelet
Tranform And
Discrete Cosine
Transform
A Survey on
Digital
Watermarking
Techniques
Watermarking with Wavelet:
Simplicity Lead to Robustness
2013 19-
27
2013 451
-
456
2008 587
-
592 embedding and extraction algorithm
A hybrid imagewatermarking technique based on DWT and SVD has been presented, where the watermark is embedded on the singular values of the cover image’s DWT sub band.
Proposed a Digital Image
Watermarking Technique
Using Discrete Wavelet
Transform and Discrete
Cosine Transform
Described a Survey on
Digital Watermarking techniques
DWT &
SVT
DWT
DCT
DWT
&
5 Evelyn
Brannock et.al.
7 P. Surekha and
Sumathi
S.
Implementation of genetic algorithm for a dwt based image watermarking scheme
2011 244
-
252
8 Kamran
Hameed et.al.
Digital Image
Watermarking in the Wavelet
Transform
2006 86-
89
Described Watermarking with Wavelet, the objective is to develop a algorithm and measure the success of algorithm by using Haar wavelet
The amplification factor of the watermark is the significant parameter that helps in improving the perceptual transparency and robustness against attacks. The tradeoff between the transparency and robustness is considered as an optimization problem and is solved by applying
Genetic Algorithm.
The author proposes a new joo’s watermarking techniques and embed watermark into wavelet
DWT
DWT,GA(G enetic
Algorithm)
DWT
8

9 Hanaa A.
10
Abdallah et.al.
Anuradha,
Rudresh
Pratap
Singh
11 Liang
Zongbao and Wang
Jiawei
12 S. Kurshid
Jinna, Dr.
L. Ganesan
Domain
Blind Wavelet-
Based Image
Watermarking
DWT Based
Watermarking
Algorithm using
Haar Wavelet
A Novel Digital
Watermark
Based on
Wavelet
Coefficients
Comparison
Lossless Image
Watermarking using Lifting
Wavelet
Transform
2011
2012
2012
15-
28
01-
06
7-
10
2009 191
-
195
DC components.
This paper proposed a wavelet based watermarking and uses blind wavelet based watermarking. In this watermark is inserted in the coarsest scale wavelet coefficiects. Wavelet divides the image into 3levels and watermark is embed into the coarsesr scale.
Proposed a discrete wavelet transform based watermarking and uses haar wavelet for copy right protection, intellectual protection and material protection.
Proposed a watermarking algorithm using 3-level wavelet transform where watermark is embedded in medium frequency component on horizontal and vertical direction based on the wavelet coefficients.
The proposed scheme uses neighboring pixel pairs to adaptively embed the watermark as byte directly. The bytes are embedded as the difference between neighboring pixel pairs whichever is expandable. Experiments are conducted to test the embedding capacity of different wavelet families.
DWT and
Quantizatio n
DWT
DWT
Integer to integer wavelet transform
9

13 Xiangui
Kang,
Wenjun
Zeng, and
Jiwu Huang
A Multi-band
Wavelet
Watermarking
Scheme
2008
-
121
126
Proposed blind watermarking in the multi- band wavelet domain can achieve higher perceptual trans- parency and stronger robustness.
Table 2. Summary on various Watermarking Techniques
Multiband wavelet transform
VII. Optimization Techniques Used for Digital Image Watermarking
Optimization techniques are used to improve the performance of watermark image using DWT.
There are various optimization techniques used like Singular Value Decomposition (SVD),
Particle Swarm Optimization (PSO), Genetic Algorithm (GA),Back Propagation Neural
Network, Fuzzy Logic and many more.
Singular Value Decomposition (SVD); Here the singular values of the image are modified to embed the watermark image by employing multiple singular functions [2].
Particle Swarm Optimization (PSO); To make an intelligent watermarking particle swarm optimization (PSO) technique is used in wavelet domain. The cover image is transformed into frequency bands and the coefficients are randomly selected from different sub bands to make up a block to increase the security so that an unauthorized party is unable to remove the watermark or to detect the existence of the watermark. This concept is called significant difference of the wavelet coefficients quantization [32].
VIII. Wavelets Analysis
The type of wavelet to be used for image watermarking depends on the following aspects:
1. Symmetry: The wavelets used should be symmetric as human visual system is less sensitive to symmetry.
2. Perfect Reconstruction: To retain the quality of watermarked image and imperceptibility, the decomposed image should be perfectly reconstructed.
Table below shows the comparison of various wavelets.
Type Filter
Haar FIR
Daubechies(db) FIR
Symlets
Coiflets
Spline
Morlet
Maxican Hat
FIR
FIR
FIR
IIR
IIR
Symmetry
Symmetric
Asymmetric
Orthogonality Fast Algorithm
Orthogonal
Orthogonal
Yes
Yes
Near Symmetric Orthogonal
Near Symmetric Orthogonal
Symmetric Biorthogonal
Symmetric
Symmetric
No
No
Yes
Yes
Yes
No
No
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Meyer
Butterworth
IIR
IIR
Symmetric
Asymmetric
Orthogonal
Orthogonal
Table 3. Main properties of wavelets.
No
Yes
The above table shows comparison of wavelet family like haar, daubechies, symlets etc. on the basis of filter, symmetry, orthogonality, and performance, in which some may be symmetric, orthogonal, and biorthogonal. Here FIR is finite impulse response, are digital filters, whose impulse response is of finite duration and IIR is infinite impulse response, are digital filters with infinite impulse response. Unlike FIR filters, IIR have the feedback and are known as recursive filter therefore.
IX. Conclusion
This paper discussed about digital image watermarking, and how to embed and extract watermark. This paper provides the review of various techniques used in watermarking like
DFT, DCT, DWT. Discrete wavelet transform overcome the limitations of DFT and DCT because DWT provides time and frequency information of the signal and passes time domain signals to various high pass and low pass filters in order to divide the image into subbands
(LL,HL,LH,HH) and then embed the watermark in the selected band. This paper shows the comparison of various literatures based on different techniques and different wavelets. Haar wavelet is the first and simplest orthogonal wavelet and provides the basis of all wavelets. In practice wavelets analysis is performed using filter banks and for watermarking we need to select an appropriate wavelet or basis. Daubechies wavelets are the most popular due to orthogonal and compact support abilities. Coiflets was derived from daubechies and having more computational overhead. In future DWT can be combined with optimization technique or artificial intelligence for better transparency and robustness.
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