Final Thesis_6 March Vikas Saxena

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DIGITAL IMAGE WATERMARKING

Thesis submitted in the fulfillment of the Degree of

Doctor of Philosophy

by

VIKAS SAXENA

Department of Computer Science and EngineeringJAYPEE INSTITUE OF INFORMATION TECHNOLOGY UNIVERSITY

A-10, SECTOR-62, NOIDA, INDIA

October, 2008


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© JAYPEE INSTITUE OF INFORMATION TECHNOLOGY UNIVERSITY, NOIDA, INDIAOctober, 2008

ALL RIGHT RESERVED


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SCHOLAR’S CERTIFICATE

This is to certify that the work reported in the Ph.D. thesis entitled “Digital Image

Watermarking” submitted at Jaypee Institute of Information Technology University,

Noida, India is an authentic record of my work carried out under the supervision of

Prof. J.P.Gupta. I have not submitted this work elsewhere for any other degree or diploma.

(Vikas Saxena)

Department of Computer Science and Engineering

Jaypee Institute of Information Technology University, Noida, India

October 10, 2008


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SUPERVISOR’S CERTIFICATE

This is to certify that the work reported in the Ph.D. thesis entitled “Digital Image

Watermarking” submitted by Vikas Saxena at Jaypee Institute of Information

Technology University, Noida, India is a bonafide record of his original work carried out

under my supervision. This work has not been submitted elsewhere for any other degree or

diploma.

(Prof. J. P. Gupta)

Vice Chancellor

Jaypee Institute of Information Technology University, Noida, India

October 10, 2008


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TABLE OF CONTENTS

PageNo.

ABSTRACT vii

ACKNOWLEDGEMENT ix

LIST OF ACCRONYMS xi

LIST OF SYMBOLS xiii

LIST OF FIGURES xv

LIST OF TABLES xix

CHAPTER-1

INTRODUCTION 1

1. 1 DATA HIDING BACKGROUND 3

1.1.1 STEGANOGRAPHY VS. WATERMARKING 5

1.1.2 CRYPTOGRAPHY VS. WATERMARKING 5

1.1.3 DIGITAL SIGNATURE VS. WATERMARKING 6

1. 2 APPLICATION AREAS OF DIGITAL WATERMARKING 71.2.1 COPYRIGHT PROTECTION 7

1.2.2 COPY PROTECTION 7

1.2.3 TEMPER DETECTION 8

1.2.4 BROADCAST MONITORING 8

1.2.5 FINGERPRINTING 9

1.2.6 ANNOTATION APPLICATIONS 9

1. 3 CHARACTERISTICS OF WATERMARKING SCHEMES 10

1. 4 TYPES OF DIGITAL WATERMARKS 11

1. 5 STRUCTURE OF THE THESIS 15


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CHAPTER-2

IMAGE WATERMARKING LITERATURE SURVEY 17

2.1 SPATIAL DOMAIN BASED WATERMARKING SCHEMES 182.1.1 LSB BASED SCHEMES 18

2.1.2 PATCH WORK BASED SCHEME 18

2.1.3 CORRELATION BASED WATERMARKING SCHEMES 19

2.1.3.1 CORRELATION BASED SCHEMES WITH 1 PN SEQUENCE 19

2.1.3.2 CORRELATION-BASED IMAGE WATERMARKING SCHEMES

WITH 2PN SEQUENCES

19

2.1.3.3 IMAGE WATERMARKING USING PRE-FILTERING 20

2.1.4 CDMA BASED IMAGE WATERMARKING SCHEME 20

2.1.5 OTHER SPATIAL DOMAIN BASED WATERMARKING SCHEMES 21

2.2 TRANSFORMED DOMAIN BASED SCHEMES 22

2.2.1 DFT BASED WATERMARKING SCHEMES 22

2.2.2 DCT BASED WATERMARKING SCHEMES 24

2.2.2.1 THE MIDDLE-BAND COEFFICIENT EXCHANGE SCHEME 26

2.2.2.2 DCT-CDMA BASED IMAGE WATERMARKING 28

2.2.3 DWT BASED WATERMARKING SCHEMES

292.2.3.1 CDMA-DWT BASED WATERMARKING SCHEME 30

2.2.3.2 DWT BASED BLIND WATERMARK DETECTION 31

2.2.3.3 DWT BASED NON-BLIND WATERMARK DETECTION 32

2.3 RECENT METHODOLOGIES 33

2.4 PROBLEM STATEMENT FORMULATION 38

2.4.1 JUSTIFICATIONS OF THE PROBLEM STATEMENT CHOSEN 40

CHPATER-3

PRELIMINARIES 45

3.1 IMAGE ENCODING STANDARDS 45

3.1.1 JPEG ENCODING 45


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3.1.2 JPEG2000 ENCODING 53

3.2 IMAGE QUALITY MEASURES 56

3.2.1 PEAK SIGNAL TO NOISE RATIO 56

3.2.2 CORRELATION COEFFICIENT 57

3.3 TEST DATA 58

CHAPTER-4

WATERMARKING OF GRAY IMAGES 61

4.1 INTRODUCTION 61

4.2 INCREASING THE ROBUSTNESS OF IMAGE WATERMARKING SCHEMES

AGAINST JPEG COMPRESSION

62

4.3 INCREASING THE ROBUSTNESS OF IMAGE WATERMARKING SCHEME

AGAINST HISTOGRAM EQUALIZATION ATTACK

64

4.4 DEVISING A COLLUSION ATTACK RESISTANT WATERMARKING

SCHEME FOR IMAGES USING DCT

68

4.4.1 G, THE POLICY GENERATOR ALGORITHM 72

4.4.2 E, THE WATERMARK EMBEDDING ALGORITHM 72

4.4.3 D, THE WATERMARK DETECTION ALGORITHM 74

4.4.4 PERFORMANCE OF THE PROPOSED SCHEME 76

4.4.4.1 PERFORMANCE AGAINST JPEG COMPRESSION 76

4.4.4.2 PERFORMANCE AGAINST COMMON IMAGE MANIPULATIONS 77

4.4.4.3 COMPARATIVE STUDY WITH OTHER MECHANISMS 77

4.5 CONCLUSION 79

CHAPTER-5

WATERMARKING of COLOR IMAGES 815.1 INTRODUCTION 81

5.2 PERFORMANCE ANALYSIS OF COLOR CHANNEL FOR DCT BASED

IMAGE WATERMARKING SCHEME

81

5.3 DEVISING AN ICAR WATERMARKING SCHEME FOR COLORED BMP

IMAGES

85


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5.3.2 COLOR CHANNEL SELECTION 87

5.3.3 E, THE WATERMARK EMBEDDING ALGORITHM 87




5.3.5.2 PERFORMANCE AGAINST COMMON IMAGE MANIPULATIONS 92

5.3.5.3 COMPARATIVE STUDY RESULTS WITH OTHER SCHEMES 93

5.4 CONCLUSION 96

CHAPTER-6

WATERMARKING OF JPEG IMAGES 97

6.1 INTRODUCTION 97

6.2 DEVELOPMG AN ICAR WATERMARKING ALGORITHM FOR JPEG

IMAGES

97


6.2.1.1 COLOR CHANNEL SELECTION 100

6.2.2 E, THE WATERMARK EMBEDDING ALGORITHM



6.2.4.1 COLOR CHANNEL SELECTION AND PERFORMANCE AGAINST

JPEG COMPRESSION

105

6.2.4.2 PERFORMANCE AGAINST IMAGE MANIPULATIONS 106

6.2.4.3 COMPARATIVE STUDY WITH SIMILAR, STATE-OF-THE-ART

SCHEMES 108

6.3 A DWT BASED WATERMARKING SCHEME FOR JPEG IMAGES 111

6.3.1 EXPLORATION OF DWT DOMAIN 112

6.3.1.1 ISSUES IN USING DWT 112

6.3.2 BACKGROUND OF THE PROPOSED SCHEME 114

6.3.3 DUAL WATERMARKING 115


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6.3.4 THE DWT BASED WATERMARKING 115

6.3.4.1 P, THE POLICY 116

6.3.4.2 G, THE POLICY GENERATOR ALGORITHM 116

6.3.4.3 E, THE WATERMARK EMBEDDING ALGORITHM 118

6.3.4.4 D, THE WATERMARK DETECTION ALGORITHM 120

6.3.5 THE DCT BASED WATERMARKING 121

6.3.6 RESULTS 121

6.3.6.1 THE VALUE OF “T” 122


6.3.6.3 PERFORMANCE AGAINST COMMON ATTACKS AND IMAGE

MANIPULATIONS

127

6.3.6.4 COMPARATIVE STUDY WITH DCT BASED SCHEMES 127

6.3.6.5 COMPARATIVE STUDY WITH DWT BASED SCHEMES 129

6.4 CONCLUSION 130

CHAPTER-7

RESULTS AND CONCLUSION 131

7.1 SUMMARY

1317.2 MAIN CONTRIBUTIONS AND HIGHLIGHTS OF THE RESULTS 131

7.3 FUTURE WORK 132

REFERENCES 135

LIST OF AUTHOR’S PUBLICATION 147

SYNOPSIS


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ABSTRACT

Watermarking has been invoked as a tool for the protection of Intellectual Property Rights(IPR) of multimedia contents. Because of their digital nature, multimedia documents can be

duplicated, modified, transformed, and diffused very easily. In this context, it is important to

develop a system for copyright protection, protection against duplication, and authentication

of contents. For this, a watermark is embedded into the digital data in such a way that it is

indissolubly tied to the data itself. Later on, such watermark can be extracted to prove

ownership to trace the dissemination of the marked work through the network, or simply to

inform users about the identity of the rights-holder or about the allowed use of data.

This thesis deals the developing the watermarking schemes for digital images stored in both,

spatial and transformed domain. In this thesis we mainly focus on the Discrete Cosine

Transform (DCT) based development. To prove its commercial usability, we take special

care so that at least one attack, having huge financial implications, can be sustained due to

the in-built capacity of the watermarking scheme. Apart from this, since JPEG is the most

commonly used image format over WWW, we pay special attention to robustness against

JPEG compression attack.

Apart from developing watermarking schemes, we also discuss the selection of color channel

to be used to carry the watermark data based on the attack that may occur most commonly on

the watermarked images. We propose to increase the robustness against some attacks by pre-

processing the images. In this thesis, we also present a correlation between the performance

of the watermarking scheme against some attacks and the original image characteristics. All

presented watermarking schemes are robust against common image manipulations and

attacks.


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ACKNOWLEDGEMENT

I am greatly indebted to my supervisor Prof. J. P. Gupta for his valuable technical guidance

and moral support through out this work. Without his support this thesis would have not been

completed.

I would also like to thank to Prof S.L Maskara, Prof Sanjay Goel and faculty members of the

department who always enlightened me by sharing their research experiences to accomplish

the quality work.

My mother provided me all support I needed to complete this thesis and other family

members specially my wife also helped me a lot in getting me this far.

Vikas Saxena

Department of Computer Science Engineering and Information Technology

Jaypee Institute of Information Technoogy University

Noida, India


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LIST OF ACCRONYMS

CC Correlation CoefficientCDMA Code Division Multiple Access

DCT Discrete Cosine Transform

DFT Discrete Fourier Transform

DWT Discrete Wavelet Transform

EBCOT Embedded Block Coding with Optimized Truncation

EZW Embedded Zero-tree Wavelet

FFT Fast Fourier Transform

HH High-High Band of DWT

HL High-Low Band of DWT

HVS Human Visual System

ICAR Inherently Collusion Attack Resistant

IPR Intellectual Property Right

JND Just Noticeable Distortion

JPEG Joint Photographic Expert Group

LH Low-High Band of DWT

LL Low-Low Band of DWT

LSB Least Significant Bit

MBCE Middle Band Coefficient Exchange

MSE Mean Square Error

PN Pseudo-random noise

PSNR Peak Signal to Noise ration

PSW Perceptually Shaped Watermarking

REL Run Length Encoding

RGB Red Green Blue

SPIHT Set Partitioning In Hierarchical Trees

SVD Singular Value Decomposition

VQ Vector Quantization


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LIST OF SYMBOLS

D Watermark detection algorithmE Watermark embedding algorithm

FH High frequency region in an 8 x 8 DCT

FL Low frequency region in an 8 x 8 DCT

FM Middle frequency region in an 8 x 8 DCT

G Policy generator algorithm

K Watermark strength parameter

P Policy

Pi An instance of a policy

Q JPEG quantization factor

S Watermark logo converted into string of ‘0’s and ‘1’s

Sr A single bit of S

T Watermark strength parameter

W Watermark logo

Wi A single bit of the watermark data

X Original cover image

Xi An instance of the cover image


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LIST OF FIGURES

Figure

No.

Caption Page

No.

1.1 Watermark on the bank currency note 4

1.2 Various classifications of watermarking 12

1.3 Image watermark embedding scheme 13

1.4 Image watermark detection scheme 13

2.1 FIR Edge Enhancement Pre-Filter 20

2.2 A General Frequency domain based watermarking model as presented

by Cox 23

2.3 Frequency regions in 8 x 8 DCT 27

2.4 JPEG Quantization matrix 28

2.5 1-Scale and 2-Scale 2-Dimensional Discrete Wavelet Transform 30

2.6 The Targeted types of to be developed watermarking schemes 43

3.1 JPEG Compression Scheme 45

3.2 An example sub image 46

3.3 Example sub image after subtracting 128 from each pixel 47

3.4 DCT of sub image shown in Figure 3.3 47

3.5 JPEG Quantization matrix 49

3.6 DCT values after quantization 49

3.7 JPEG Decompression Scheme 51

3.8 DCT values regenerated in decompression 51

3.9 (a) Sub image pixel values (still shifted down by 128) 51

3.9 (b) Decompressed sub image pixel values 52

3.10 Error matrix for example sub image 523.11 Test images of Lena, Mandrill, Pepper and Barbara (Gray) 58

3.12 Test images of Lena, Mandrill, Pepper and Goldhill (Colored) 59

3.13 Watermark logo used in the proposed schemes 59


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4.1 (a) Extracted watermark logos from test images of Lena, Mandrill and

Pepper by applying DCT based scheme 62

4.1 (b) Extracted watermark logos from test images of Lena, Mandrill and

Pepper by applying DWT based scheme 62

4.2 (a) Extracted watermark logos from test images of Lena, Mandrill,

Pepper and Barbara by applying DCT based scheme 66

4.2 (b) Extracted watermark logos from test images of Lena, Mandrill,

Pepper and Barbara by applying DWT based scheme 66

4.3 Extracted logos from “original image” (left) and “transformed image”

(right) of Lena, Mandrill, Pepper and Barbara’s (Top to Bottom)

histogram equalized images (By applying DCT based scheme) 68

4.4 Swapping of 4 pairs to hide “0” or “1” in conjunction with low

frequency values 71

4.5 Extracted watermark logos after JPEG compression at Q = 20 from

watermarked Lena, Mandrill and Pepper images 77

4.6 Extracted watermark logos from Lena’s image after Horizontal

flipped, scaled, brightness /contrast adjusted and Noising (Left to

Right, Top to bottom) 784.7 Percentage decrease in quality of extracted watermark with respect to

JPEG quality factor 79

5.1 Recovered watermarks for Lena.bmp after jpeg attack at Q = 40 82

5.2 Watermarked test images keeping T = 150 91

5.3 Extracted watermark from watermarked Lena, Mandrill and Pepper

images respectively at T = 150 91

5.4 Recovered logos from attacked images 94

5.5 Extracted logos using proposed scheme from highly compressed

watermarked test images 95

6.1 Watermarked test images generated by keeping T = 150 105

6.2 Extracted watermark logos from watermarked Lena, Mandrill, Pepper

and Goldhill test images respectively at T = 150 105


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6.3 Goldhill test image after hiding the watermark logo and the recovered

logo at T = 100 105

6.4 Extracted logos from attacked watermarked images 109

6.5 Comparison of correlation coefficients at Q = 10 110

6.6 Comparison of correlation coefficients at Q = 5 111

6.7 2-D Haar DWT 113

6.8 An example of 2 consecutive DWT blocks 117

6.9 An example of 2 consecutive DWT blocks 117

6.10 Watermark embedding approach 120

6.11 The watermark logo 122

6.12 Graph of the values shown in Table 6.6 123

6.13 Extracted logos from Lena, Mandrill and Pepper’s test images 124

6.14 The extracted logos using DWT based method 125

6.15 The extracted logos using DCT based method 126

6.16 Extracted logos from highly compressed JPEG images 126

6.17 Extracted watermark logos after applying common attacks 128


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LIST OF TABLES

Table

No.

Caption Page

No.4.1 PSNR (in decibel) of extracted watermark logo from JPEG

compressed (Q = 20) watermarked image 65

4.2 PSNR of extracted logos from attacked test images 67

4.3 PSNR of extracted watermarks after JPEG compression 77

5.1 PSNR of Extracted watermark from JPEG compressed watermark test

images 82

5.2 PSNR of extracted watermark from attacked watermarked test images 84

5.3 PSNR of extracted watermark logos after JPEG compression 92

5.4 PSNR of extracted watermark logo from watermarked test images

after attacks 93

5.5 PSNR values of extracted logos from highly compressed watermarked

test images using various schemes 95

6.1 SD values of color channels for test images 106

6.2 PSNR and CC of extracted logo by using BLUE channel for all

images 1076.3 PSNR and CC of extracted logo by using BLUE and GREEN

channels for images 108

6.4 CC of the extracted logos 108

6.5 PSNR of watermarked image and CC of extracted logo for various

values of T 122

6.6 Revised Table 6.5 122

6.7 CC of extracted logos from JPEG2000 attacked images 124

6.8 Decrement in the PSNR values after the application of DCT based

scheme 125

6.9 CC values of the extracted watermark logos recovered by both

recovery methods 125


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6.10 CC of extracted logo from highly compressed jpeg image using DCT

based recovery 126

6.11 CC of the extracted watermark logos 128

6.12 Comparison of CC of Extracted logos from JPEG compressed

(Q = 10) watermarked images 129

6.13 Comparison of CC of Extracted logos from JPEG compressed

(Q = 5) watermarked images 129


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CHAPTER 1

INTRODUCTION

The growth of high speed computer networks and World Wide Web (WWW) have explored

means of new business, scientific, entertainment and social opportunities in the form of

electronic publishing and advertising, massaging, real-time information delivery, data

sharing, collaboration among computers, product ordering, transaction processing, digital

repositories and libraries, web newspapers and magazines, network video and audio, personal

communication and lots more. The cost effectiveness of selling softwares in the form ofdigital images and video sequences by transmission over WWW is greatly enhanced due to

the improvement in technology.

We know that one of the biggest technological events of the last two decades was the

invasion of digital media in an entire range of everyday life aspects. Digital data can be

stored efficiently and with a very high quality, and it can be manipulated very easily using

computers. Furthermore, digital data can be transmitted in a fast and inexpensive way

through data communication networks without losing quality. Digital media offer several

distinct advantages over analog media. The quality of digital audio, images and video

signals are higher than that of their analog counterparts. Editing is easy because one can

access the exact discrete locations that need to be changed. Copying is simple with no loss

of fidelity. A copy of a digital media is identical to the original. With digital multimedia

distribution over World Wide Web, authentications are more threatened than ever due to the

possibility of unlimited copying. The easy transmission and manipulation of digital data

constitutes a real threat for information creators, and copyright owners want to becompensated every time their work is used. Furthermore, they want to be sure that their

work is not used in an improper way (e. g. modified without their permission). For digital

data, copyright enforcement and content verification are very difficult tasks. One solution

would be to restrict access to the data using some encryption techniques. However,


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watermarking, requirements that watermarking system must meet, types of the watermarking,

applications and then various attacks on a watermarking system.

1.1 DATA HIDING BACKGROUND

The solution of the problem discussed above seems to lie in a technique that dates back to

ancient Egypt and Greece: data hiding or steganography. Steganography deals with the

methods of embedding data within a medium (host or cover medium) in an imperceptible

way. All forms of digital data (still images, audio, video, text documents and multimedia

documents) can be used as a cover medium for information hiding.

The history of steganography goes all the way back to the 5th Century. The earliest known

writings about steganography were by the Greek historian Herodotus. The historian relates

how a slave had a message tattooed on his head by Histiaeus who was trying to get a

message to his son-in-law Aristagoras. Once the slaves’ hair was long enough to cover the

message he was sent to Aristagoras in the city of Miletus [92].

Stegnography has been used in many different ways. The simplest was the use of invisible

inks that a person could use to send a message to another person without anyone elseknowing. Different forms of invisible ink were used to conceal messages. Some of the more

common forms of invisible ink have been lemon juice, milk, and urine to name a few. If

someone wanted to conceal a message, he would simply write a message, using one of these

inks, on a sheet of paper that already had something written on it. The person receiving the

message would then hold the paper over a flame and the transparent message would appear.

Image stegnography was done during the early twentieth century. During the Boer War in

South Africa, the British were using Lord Robert Baden-Powell as a scout. He was scouting

the Boer artillery bases mapping their positions. He took his maps and converted them into

pictures of butterflies with certain markings on the wings that were actually the enemies’

positions [92].


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During World War II, Nazis introduced a new concept in espionage, which was called the

microdot. This simple device could conceal a full typewritten page within the size of a

common period. A microdot could hold valuable information such as charts, diagrams and

drawings.

Figure 1.1: Watermark on the bank currency note

Thus, stegnography is an area which is, more or less, a Hide-&-Seek game. Some important

data or information is hidden in another medium. The cover medium has no relationship

with the data or information hidden. Data or information which is hidden is not encrypted

also. The key issue in a stegnography system becomes that no one should suspect that a

particular medium is carrying any hidden data or information.

We can extend the stegnography concept for the authentication of digital multimedia data.

Digital multimedia data which has to be protected is now the cover medium and then we can

hide the copyright data into it. In this case, there will be two major requirements as follows:

1) Imperceptibility: After hiding the copyright data, cover medium should not be

affected, and

2) Robustness: No body should be able to remove the data without affecting the cover

medium.

Watermarksymbol isadded here to prove theoriginality


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The copyright data may be termed as digital watermark data. This area of application of

stegnography is known as Digital Watermarking. Therefore, digital watermark is a

message/data/information which is embedded into digital content (audio, video, images or

text) that can be detected or extracted later. Such message/data/information mostly carries

the copyright or ownership information of the content. The process of embedding digital

watermark information into digital content is known digital watermarking.

Before moving further in this discussion, we must first understand the difference of the

digital watermarking with other related terms like stegnography, cryptography and digital

signature.

1.1.1 STEGANOGRAPHY VS WATERMARKING

Watermarking is the subset of Stegnography. In Stegnography, data which is hidden has no

relationship with the cover medium and the requirement from such a system is that no

suspicion should arise that a medium is carrying any hidden data. In watermarking, unlike

stegnography, the data which is hidden has relationship with the cover medium data. Data

hidden is the ownership data of the cover medium and there is no issue like suspecting that a

particular medium is carrying some copyright data.

As the purpose of stegnography is to have a covert communication between two parties i.e.

existence of the communication is unknown to a possible attacker, and a successful attack

shall detect the existence of this communication. On the contrary, watermarking, as opposed

to stegnography, requires a system to be robust against possible attacks. Other requirements

of watermarking are entirely different from stegnography and these are discussed in detail in

Section 1.3.

1.1.2 CRYPTOGRAPHY VS. WATERMARKING

Cryptography can be defined as the processing of information into an unintelligible form

known as encryption, for the purpose of secure transmission. Through the use of a “key”,

the receiver can decode the encrypted message (the process known as decryption) to retrieve


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the original message. So, cryptography is about protecting the contents of the message. But

as soon as the data is decrypted, all the in-built security and data is ready to use.

Cryptography "scrambles" a message so that it can not be understood by unauthorized user.

This does not happen in watermarking. Neither the cover medium nor the copyright data

changes its meaning. Rather, copyright data is hidden to give the ownership information of

the medium in which it is hidden.

1.1.3 DIGITAL SIGNATURE VS. WATERMARKING

Digital signatures, like written signatures, are used to provide authentication of the associated

input, usually called a "message”. Digital signature is an electronic signature that can be used

to authenticate the identity of the sender of a message or the signer of a document, and possibly to ensure that the original content of the message or document that has been sent is

unchanged. Digital signatures are easily transportable, cannot be imitated by someone else,

and can be automatically time-stamped. The ability to ensure that the original signed

message arrived means that the sender cannot easily repudiate it later. A digital signature can

be used with any kind of message, whether it is encrypted or not, simply so that the receiver

can be sure of the sender's identity and that the message arrived intact. A digital signature is

apart from the protected message, whereas a digital watermark is inside a multimediamessage. Both, digital signature and watermarking protect integrity and authenticity of a

document. Digital signature system is vulnerable to distortion but a watermark system has to

tolerate a limited distortion level.

So, to conclude, Watermarking is adding“ownership” information in multimedia contents to

prove the authenticity. This technology embeds a data, an unperceivable digital code,

namely the watermark, carrying information about the copyright status of the work to be

protected. Continuous efforts are being made to device efficient watermarking schema but

techniques proposed so far do not seem to be robust to all possible attacks and multimedia

data processing operations. The sudden increase in watermarking interest is most likely due

to the increase in concern over IPR. Today, digital data security covers such topics as access

control, authentication, and copyright protection for still images, audio, video, and


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multimedia products. A pirate tries either to remove a watermark to violate a copyright or to

cast the same watermark, after altering the data, to forge the proof of authenticity. Generally,

the watermarking of still images, video, and audio demonstrate certain common fundamental

concepts.

1. 2 APPLICATION AREAS OF DIGITAL WATERMARKING

Watermarking techniques may be relevant in the following application areas [26]:

1.2.1 COPYRIGHT PROTECTION

The primary use of watermarking is where an organization wishes to assert its ownership of

copyright for digital objects. This application is of great interest to ‘big media’

organizations, and of some interest to other vendors of digital information, such as news and

photo agencies. These applications require a minimal amount of information to be

embedded, coupled with a high degree of resistance to signal modification (since they may

be subjected to deliberate attack). For example, now a days, a news channel “AAJ-TAK” is

showing the animal’s clips (which are already shown on “Discovery” Channel) by hiding the

Discovery channel’s logo on the video clips. As per the law, The AAJ-TAK should show the

curtsey-sign and should pay the copyright fee to the Discovery channel. In such cases,There is a strong need of watermarking as once the digital data is broadcasted, any body else

can start selling it without paying the IPR value to its owner.

1.2.2 COPY PROTECTION

Watermarking can be used as a strong tool to prevent illegal copying. For example, if an

audio CD has a watermark embedded into it, then any of the system (Hardware like DVD, or

software) can not make a copy of it, and even if it copies, the watermark data will not getcopied to new duplicate audio CD. Now the duplicate CD can be easily found because it

does not have watermark data. Some schemes have attempted to satisfy more complex copy

protection requirements. An early example is the Serial Copy Management System (SCMS),

introduced in the 1980s, which enabled a user to make a single digital audio tape of a

recording they had purchased but prevented the recording of further copies (i.e. second


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generation) from that first copy. The scheme failed ultimately because not all manufacturers

of consumer equipment were prepared to implement the scheme in their products.

1.2.3 TEMPER DETECTION

In this application area, it is necessary to assure that the origin of a data object is

demonstrated and its integrity is proved. One example of temper detection is photographic

forensic information which may be presented as evidence in the court. Given the ease with

which digital images can be manipulated, there is a need to provide proof that an image has

not been altered. Such a mechanism could be built into a digital camera [29]. For example,

if a cop’s camera catches an over speeding vehicle then when proving the driver guilty in

front of the judge, the accused may claim that the video presented in the court is temperedand the car shown in the video does not belong to him. A watermarking system which is

embedded in digital cameras may help to resolve the issue. If somebody tries to temper the

data, the watermark will get destroyed indicating that the data is tempered. In our country, a

well-known example is the “Tahalka-Scam”.

1.2.4 BROADCAST MONITORING

There are several types of organizations and individuals interested in monitoring the broadcast of their interest. For example, advertisers want to ensure that they receive the exact

airtime that they have purchased from broadcasting firms. Musicians and actors want to

ensure that they receive accurate royalty payments for broadcasts of their performances and

copyright owners want to ensure that their property is not illegally rebroadcast by pirate

stations. In 1997, a scandal broke out in Japan regarding television advertising. At least two

stations had been routinely overbooking air time. Advertisers were paying for thousands of

commercials that were never aired [16]. The practice had remained largely undetected for

over twenty years because there were no systems in place to monitor the actual broadcast of

advertisements.


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This broadcast monitoring can be implemented by putting a unique watermark in each video

or sound clip prior to broadcast. Automated monitoring stations can then receive broadcasts

and look for these watermarks identifying when and where each clip appears.

1.2.5 FINGERPRINTING

If monitoring and owner identification applications place the same watermark in all copies of

the same content, it may create a problem. If out of n number of legal buyers of a content,

one starts selling the contents illegally, it may be very difficult to catch who is redistributing

the contents without permission. Allowing each copy distributed to be customized for each

legal recipient can solve this problem. This capability allows a unique watermark to be

embedded in each individual copy. Now, if the owner finds an illegal copy, he can find outwho is selling his contents by finding the watermark which belongs to only singly legal

buyer. This particular application area is known as fingerprinting. This is potentially

valuable both as a deterrent to illegal use and as a technological aid to investigation.

1.2.6 ANNOTATION APPLICATIONS

In this applications area, watermarks convey object-specific information (“feature tags” or

“captions”) to users of the object. For example, patient identification data can be embedded

into medical images. These applications require relatively large quantities of embedded data.

While there is no need to protect against deliberate tampering. Normal use of the data object

may involve such transformations as image cropping or scaling and will require the use of a

technique that is resistant to those types of modification.

For more details of various watermarking applications, one may refer [20].


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1. 3 CHARACTERISTICS OF WATERMARKING SCHEMES

An effective watermarking scheme should have the following characteristics:

1) Imperceptibility: In terms of watermarking, imperceptibility means that after inserting

the watermark data, cover medium should not alter much. In other words, the

presence of the watermark data should not affect the cover medium being protected.

If a watermarking scheme does not ensure this requirement, it may happen that after

inserting a watermark data in a cover medium (say an image), image quality may alter

which the owner of the image will never like that a protecting mechanism modifies

his work.

2) Robustness: Robustness of the watermark data means that the watermark data should

not be destroyed if someone performs the common manipulations as well as

malicious attacks. It is more of a property and also a requirement of watermarking

and its applicability depends on the application area.

3) Fragility: Fragility means that the watermark data is altered or disturbed up to a

certain extent when someone performs the common manipulations & malicious

attacks. Some application areas like temper detection may require a fragile

watermark to know that some tempering is done with his work. Some applicationmay require semi-fragility too. The semi-fragile watermark comprises a fragile

watermark component and a robust watermark component i.e. semi-fragile

watermarks are robust to some attacks but fragile to others attacks.

4) Resilient to common signal processing: The watermark should be retrievable even if

common signal processing operations are applied to the watermarked cover medium

data. These operations include digital-to-analog and analog-to-digital conversion (i.e.

taking the printout of an image and then scan it to create another digital copy of the

image), re-sampling, re-quantization (including dithering and recompression), and

common signal enhancements such as image contrast, brightness and color

adjustment, or audio bass and treble adjustment, high pass and low pass filtering,

histogram equalization of an image and format conversion (BMP image to JPEG

image, MPEG movie to WMV movie, mp3 song to mp4 etc.)


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5) Resilient to common geometric distortions (image and video data): Watermarks in

image and video data should also be immune from geometric image operations such

as rotation, translation, cropping and scaling. This property is not required for audio

watermarking.

6) Robust to subterfuge attacks (collusion and forgery): In addition, the watermark

should be robust to collusion attack. Multiple individuals, who possess a watermarked

copy of the data, may collude their watermark copies to destroy the watermark

presence and can generate a duplicate of the original copy. Further, if a digital

watermark is to be used in litigation, it must be impossible for colluders to combine

their images to generate a different valid watermark.

7) Unambiguousness: Retrieval of the watermark should unambiguously identify theowner. Furthermore, the accuracy of owner identification should not degrade much

in the case of an attack. The Unzign and Stirmark [97] have shown remarkable

success in removing data embedded by commercially available programs.

Watermarking of watermarked image (re-watermarking) is also a major threat [97].

1.4 TYPES OF DIGITAL WATERMARKS

Prof. S. Mohanty presents a very good classification of watermarking areas in his paper [62].

We can classify the types of watermarking based on the cover medium, embedding domain,

perception and application domain. Figure 1.2 shows the various classifications of

watermarking.

Based on their embedding domain, watermarking schemes can be classified as follows:

1) Spatial Domain: The watermarking system directly alters the main data elements (like

pixels in an image) to hide the watermark data.

2) Transformed Domain: The watermarking system alters the frequency transforms of

data elements to hide the watermark data. This has proved to be more robust than the

spatial domain watermarking.


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3) Feature Domain: The watermarking system takes into account the region, boundary

and object characteristics. It presents better detection and recovery from attacks.

Figure 1.2: Various classifications of watermarking

Watermarking techniques can also be divided into four categories, according to the type ofdocument to be watermarked, as follows.

1) Image Watermarking: Figure 1.3 and 1.4 represent the general scheme of an image

watermarking, embedding and decoding (specifically key based, invisible and fragile)


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According to application domain, Source-based watermarks are desirable for ownership

identification or authentication where a unique watermark identifies the owner. A source-

based watermark could be used for authentication and to determine whether a received image

or other electronic data has been tampered. The watermark could also be destination based

where each distributed copy gets a unique watermark identifying the particular buyer. The

destination based watermark could be used to trace the buyer in the case of illegal reselling.

This is used in fingerprinting. A watermark is said private if only authorized readers can

detect it. In other words, in private watermarking, a mechanism is envisaged that makes it

impossible for unauthorized people to extract the watermark. A watermarking algorithm is

said blind if it does not resort to the comparison between the original non-marked and the

marked document to recover the watermark. Conversely, a watermarking algorithm is saidnon-blind if it needs the original data to extract the information contained in the watermark.

The definition of invertible and quasi-invertible is more technical and can be given as

follows [2]:

If E is the Embedding algorithm, D is detection algorithm, Cδ is Comparator function, I is

original cover image, Î is watermarked image, J is recovered attacked image, S is watermarksignal and S’ is extracted watermark data, then:

1) E (I, S) = Î

2) D (J, I) = S’ or D (J) = S’

3) Comparator Cδ:

A watermarking scheme (E, D, Cδ) is invertible if:

1) Inverse mapping E-1 does exist such that E-1 (Î) = (Î’, S’) &E (Î’, S’) = Î;

2) E-1 is computational feasible;


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3) S’ is an allowed watermark;

4) Î and Î’ are perceptually similar; and

5) Comparator output Cδ (D (Î, Î’), S’) = 1

Otherwise the watermarking scheme is non-invertible.

A watermarking scheme (E, D, Cδ) is quasi-invertible if:

1) Properties for invertible watermarking schemes apply;

2) Only difference E (Î’, S’) = Î’’ ≠ Î; and

3) Î’’ and Î perceptually similar.

Otherwise the watermarking scheme is non-quasi-invertible. A Non-invertible scheme can

be quasi-invertible and Non-quasi-invertibility implies non-invertibility.

1.5 STRUCTURE OF THE THESIS

This thesis comprises of the following chapters:

Chapter 2 describes the image watermarking literature survey and problem statement.

Chapter 3 describes the preliminaries (like background of JPEG compression, 2D–DCT and

DWT, image quality parameter, some standard watermarking techniques which are used to

compare the performances of the proposed techniques etc and test images data). The

watermarking techniques for gray images have been proposed in Chapter 4. Chapter 5

describes the proposed watermarking techniques and issues related to colored BMP images.

In Chapter 6, the proposed watermarking techniques for JPEG images have been given.

Finally the summary of results, conclusions and future work is given in Chapter 7 followed

by references, author’s publications and synopsis at the end.


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CHAPTER-2

IMAGE WATERMARKING LITERATURE SURVEY

Within the field of watermarking, image watermarking particularly has attracted lot of

attention in the research community. Most of the research work is dedicated to image

watermarking as compared to audio and video. There may be 3 reasons for it. Firstly,

because of ready availability of the test images, secondly because it carries enough

redundant information to provide an opportunity to embed watermarks easily, and lastly,

it may be assumed that any successful image watermarking algorithm may be upgraded

for the video also.

Images are represented/stored in spatial domain as well as in transform domain. The

transform domain image is represented in terms of its frequencies; whereas, in spatial

domain it is represented by pixels. In simple terms, transform domain means the image is

segmented into multiple frequency bands. To transfer an image to its frequency

representation, we can use several reversible transforms like Discrete Cosine Transform

(DCT), Discrete Wavelet Transform (DWT), or Discrete Fourier Transform (DFT). Each

of these transforms has its own characteristics and represents the image in different ways.

Watermarks can be embedded within images by modifying these values, i.e. the

transform domain coefficients. In case of spatial domain, simple watermarks could be

embedded in the images by modifying the pixel values or the Least Significant Bit (LSB)

values. However, more robust watermarks could be embedded in the transform domain of

images by modifying the transform domain coefficients. In 1997 Cox et al. presented a

paper “Secure Spread Spectrum Watermarking for Multimedia” [19], one of the most

cited paper (cited 2985 times till April’ 2008 as per Google Scholar search), and after thatmost of the research work is based on this work. Even though spatial domain based

techniques can not sustain most of the common attacks like compression, high pass or

low pass filtering etc., researchers present spatial domain based schemes. Firstly, brief


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introductions of some classical well-known spatial domain based schemes are being

given as follows [19]:

2.1 SPATIAL DOMAIN BASED WATERMARKING SCHEMES

2.1.1 LSB BASED SCHEMES

In their paper, Macq and Quisquater [60] briefly discussed the issue of watermarking

digital images as part of a general survey on cryptography and digital television. The

authors provided a description of a procedure to insert a watermark into the least

significant bits of pixels located in the vicinity of image contours. Since it relies on

modifications of the least significant bits, the watermark is easily destroyed. Further, their

method is restricted to images, in that it seeks to insert the watermark into image regions

that lie on the edge of contours.

Rhoads [79] described a method that adds or subtracts small random quantities from each

pixel. Addition or subtraction is determined by comparing a binary mask of bits with the

LSB of each pixel. If the LSB is equal to the corresponding mask bit, then the random

quantity is added, otherwise it is subtracted. The watermark is subtracted by first

computing the difference between the original and watermarked images and then by

examining the sign of the difference, pixel by pixel, to determine if it corresponds to the

original sequence of additions and subtractions. This method does not make use of

perceptual relevance, but it is proposed that the high frequency noise be prefiltered to

provide some robustness to lowpass filtering. This scheme does not consider the problem

of collusion attacks.

2.1.2 PATCH WORK BASED SCHEMES

Another, well known spatial domain based scheme is patchwork-based technique given

by Bender et al. [7]. They described two watermarking schemes. The first is a statistical

method called patchwork . Patchwork randomly chooses pairs of image points, and

increases the brightness at one point by one unit while correspondingly decreasing the

brightness of another point. The second method is called “texture block coding” wherein


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a region of random texture pattern found in the image is copied to an area of the image

with similar texture. Autocorrelation is then used to recover each texture region. The

most significant problem with this scheme is that it is only appropriate for images that

possess large areas of random texture. The scheme could not be used on images of text.

Other Patchwork based algorithm can be found in [110, 124].

2.1.3 CORRELATION BASED WATERMARKING SCHEMES

The most straightforward way to add a watermark to an image in the spatial domain is to

add a pseudorandom noise pattern to the luminance values of its pixels. Many methods

are based on this principle [6, 11, 27, 33-34, 53, 68, 70, 91, 95, 114-117].

2.1.3.1 CORRELATION BASED SCHEMES WITH 1 PN SEQUENCE: A well

known technique for watermark embedding is to exploit the correlation properties of

additive pseudo-random noise patterns as applied to an image [42, 52]. A Pseudo-random

Noise (PN) pattern W (x, y) is added to the cover image I (x, y), according to the

Equation 2.1 given below:

),(*),(),( y xW k y x I y x I w += ……………………………………………………… (2.1)

In Equation 2.1, k denotes a gain factor and IW the resulting watermarked image.

Increasing k increases the robustness of the watermark at the expense of the quality of the

watermarked image. To retrieve the watermark, the same pseudo-random noise generator

algorithm is seeded with the same key, and the correlation between the noise pattern and

possibly watermarked image is computed. If the correlation exceeds a certain threshold T,

the watermark is detected, and a single bit is set. This method can easily be extended to a

multiple-bit watermark by dividing the image into blocks and performing the above

procedure independently on each block.

2.1.3.2 CORRELATION-BASED IMAGE WATERMARKING SCHEMES WITH

2PN SEQUENCES: This basic algorithm, as given in previous section, can be improved

in a number of ways. First, the notion of a threshold being used for determining a logical


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“1” or “0” can be eliminated by using two separate pseudo-random noise patterns. One

pattern is designated a logical “1” and the other a logical “0”. The above procedure is

then performed once for each pattern, and the pattern with the higher resulting correlation

is used. This increases the probability of correct detection, even after the image has been

subject to attack [42, 52].

2.1.3.3 IMAGE WATERMARKING USING PRE-FILTERING: We can further

improve the basic algorithm by pre-filtering the image before applying the watermark. If

we can reduce the correlation between the cover image and the PN sequence, we can

increase the immunity of the watermark to additional noise. By applying the edge

enhancement filter shown below in Figure 2.1, the robustness of the watermark can be

improved with no loss of capacity and very little reduction of image quality [42, 52].

−−−

−−

−−−

=

111

1101

111

2

1edge F

Figure 2.1: FIR Edge Enhancement Pre-Filter

2.1.4 CDMA BASED IMAGE WATERMARKING SCHEME

Rather than determining the values of the watermark from “blocks” in the spatial domain,

we can employ CDMA spread-spectrum schemes to scatter each of the bits randomly

throughout the cover image, thus increasing capacity and improving resistance to

cropping. The watermark is first formatted as a long string rather than a 2D image. For

each value of the watermark, a PN sequence is generated using an independent seed.

These seeds could either be stored or themselves generated through PN methods. The

summation of all of these PN sequences represents the watermark, which is then scaledand added to the cover image [42, 52].

To detect the watermark, each seed is used to generate its PN sequence which is then

correlated with the entire image. If the correlation is high, that bit in the watermark is set

to “1”, otherwise a “0”. The process is then repeated for all the values of the watermark.


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CDMA improves on the robustness of the watermark significantly but it requires more

computation.

2.1.5 OTHER SPATIAL DOMAIN BASED WATERMARKING SCHEMES

In [104], a method that embeds a binary watermark image in the spatial domain is

proposed. A spatial transform that maps each pixel of the watermark image to a pixel of

the host image, is used. Chaotic spread of watermark image pixels in the host image is

achieved by “toral automorphisms”. For watermark embedding, the intensity of the

selected pixels is modified by an appropriate function that takes into account

neighborhood information in order to achieve watermark robustness to modifications. For

detection, a suitable function is applied on each of the watermarked pixels to determine

the binary digit (0 or 1) that has been embedded. The inverse spatial transform is thenused to reconstruct the binary watermark image.

In the method proposed in [69], the image is split into two random subsets A and B and

the intensity of pixels in A is increased by a constant embedding factor k. Watermark

detection is performed by evaluating the difference of the mean values of the pixels in

subsets A and B. This difference is expected to be equal to k for a watermarked image

and equal to zero for an image that is not watermarked. Hypothesis testing can be used todecide for the existence of the watermark. The above algorithm is vulnerable to lowpass

operations. Extensions to above algorithm are proposed in [64]. According to this

paper, the robustness of the method can be increased by grouping pixels so as to form

blocks of certain dimensions to enhance the low pass characteristics of the watermark

signal. Alternatively, one can take advantage of the fact that different embedding factor

can be used for each pixel, to shape appropriately the watermark signal. An optimization

procedure that calculates the appropriate embedding value for each pixel so that the

energy of the watermark signal is concentrated at low frequencies is proposed.

Constraints that ensure that the watermark signal is invisible can be incorporated in the

optimization procedure.


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In [45] the authors derived analytical expressions for the probabilities P-, P+ of false

negative and false positive watermark detection. Their model assumes an additive

watermark and a correlator-based detection stage. Both, the white watermarks and

watermarks having low pass characteristics, are considered. The host image is treated as

noise, assuming a first order separable autocorrelation function. The probabilities P-, P+

are expressed in terms of the watermark to image power ratio. The authors conclude that

detection error rates are higher for watermarks with low pass characteristics.

In last 12 years, number of publications in this area is increasing very rapidly and no

survey can cover all the presented schemes, but there are some very good survey papers

and interested reader may explore the papers [3, 13, 54, 76]. We are limiting the

discussion of the spatial domain based schemes here.

2.2 TRANSFORMED DOMAIN BASED SCHEMES

As presented in literature, transformed domain based watermarking schemes are more

robust as compared to simple spatial domain watermarking schemes. Such algorithms are

robust against simple image processing operations like low pass filtering, brightness and

contrast adjustment, blurring etc. However, they are difficult to implement and are

computationally more expensive. We can use either of Discrete Fourier Transform(DFT), Discrete Cosine Transform (DCT) or Discrete Wavelet Transform (DWT) but

DCT is the most exploited one. A General transformed domain based scheme, as

presented by Cox, is shown in Figure 2.2. A very good discussion on DCT/DWT/DFT

based watermarking schemes is given in [76].

2.2.1 DFT BASED WATERMARKING SCHEMES

We start from DFT. There are few algorithms that modify these DFT magnitude and

phase coefficients to embed watermarks. Ruanaidh et al. proposed a DFT watermarking

scheme in which watermark is embedded by modifying the phase information within the

DFT. It has been shown that phase based watermarking is robust against image contrast

operation [114]. Later Ruanaidh and Pun showed how Fourier Mellin transform could be


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used for digital watermarking. Fourier Mellin transform is similar to applying Fourier

Transform to log-polar coordinate system for an image.

This scheme is robust against geometrical attacks [116]. De Rosa et al. proposed a

scheme to insert watermark by directly modifying the mid frequency bands of the DFT

magnitude component [115]. Ram kumar et al. also presented a data hiding scheme based

on DFT, where they modified the magnitude component of the DFT coefficients. Their

simulations suggest that magnitude DFT survives practical compression which can be

attributed to the fact that most practical compression schemes try to maximize the PSNR.

Hence using magnitude DFT is a way to exploit the hole in most practical compression

schemes.

Figure 2.2: A General Frequency domain based watermarking model as presented by Cox [19]


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The proposed scheme is shown to be resistant to Joint Photographic Expert Group

(JPEG) and (Set Partitioning In Hierarchical Trees) SPIHT compression [68]. Lin et al.

presented a RST resilient watermarking algorithm. In their algorithm, the watermark is

embedded in the magnitude coefficients of the Fourier transform re-sampled by log-polar

mapping. The scheme is, however, not robust against cropping and shows weak

robustness against JPEG compression (Q = 70) [53]. Solachidis and Pitas presented a

novel watermarking scheme. They embed a circularly symmetric watermark in the

magnitude of the DFT domain [8]. Since the watermark is circular in shape with its centre

at image center, it is robust against geometric rotation attacks. The watermark is centered

around the mid frequency region of the DFT magnitude. Neighborhood pixel variance

masking is employed to reduce any visible artifacts. The scheme is computationally not

expensive to recover from rotation. Robustness against cropping, scaling, JPEGcompression, filtering, noise addition and histogram equalization is demonstrated. A

semi-blind watermarking scheme has been proposed by Ganic and Eskicioglu [30]. They

embed circular watermarks with one in the lower frequency while the other is in the

higher frequency.

2.2.2 DCT BASED WATERMARKING SCHEMES

DCT domain watermarking can be classified into Global DCT watermarking and Block

based DCT watermarking. One of the first algorithms presented by Cox et al. [19] used

global DCT approach to embed a robust watermark in the perceptually significant portion

of the Human Visual System (HVS). Embedding in the perceptually significant portion of

the image has its own advantages because most compression schemes remove the

perceptually insignificant portion of the image. In spatial domain it represents the LSB.

However in the frequency domain it represents the high frequency components.

As described in [76], steps in DCT Block Based Watermarking Algorithm are:

1) Segment the image into non-overlapping blocks of 8x8;

2) Apply forward DCT to each of these blocks;

3) Apply some block selection criteria (e.g. HVS);


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4) Apply coefficient selection criteria (e.g. highest);

5) Embed watermark by modifying the selected coefficients; and

6) Apply inverse DCT transform on each block.

Most DCT based algorithms differ with each other on account of step 3 and 4 i.e. they

differ either in the block selection criteria or coefficient selection criteria. Initially, Koch,

Rindfrey, and Zhao [7] proposed a method for watermarking images. In that method, they

break up an image into 8x8 blocks and compute discrete cosine transform (DCT) of each

of these blocks. A pseudorandom subset of the blocks is chosen and then in each such

block, a triplet of frequencies is selected from one of 18 predetermined triplets and

modified so that their relative strengths encode a ‘1’ or ‘0’ value. The 18 possible triplets

are composed by selection of three out of eight predetermined frequencies within the 8x8DCT block. The choice of the eight frequencies to be altered within the DCT block is

based on a belief that the “middle frequencies have moderate variance,” i.e. they have

similar magnitude. This property is used to allow the relative strength of the frequency

triplets to be altered without requiring a modification that would be perceptually

noticeable.

Several DCT based schemes are presented in [8, 17-19, 21, 37, 71, 74, 81, 99, 118].

Using the DCT, an image can easily be split up in pseudo frequency bands so that the

watermark can conveniently be embedded in the most important middle band frequencies.

Furthermore, the sensitivity of the HVS to the DCT based images has been extensively

studied, which resulted in the recommended JPEG quantization Table [112]. These

results can be used for predicting and minimizing the visual impact of the distortion

caused by the watermark. Finally, the block-based DCT is widely used for image and

video compression. By embedding a watermark in the same domain as the compression

scheme used to process the image (in this case in the DCT domain), we can anticipate

lossy compression because we are able to anticipate which DCT coefficients will

be discarded by the compression scheme. Furthermore, we can exploit the DCT

decomposition to make real-time watermark applications.


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Further improvements for DCT-domain correlation-based watermarking systems'

performance could be achieved by using watermark detectors based on generalized

Gaussian model instead of the widely used pure Gaussian assumption [35]. By

performing a theoretical analysis for DCT-domain watermarking methods for images, the

authors in [35] provided analytical expressions which could be used to measure

beforehand the performance expected for a certain image and to analyze the influence of

the image characteristics and system parameters (e.g. watermark length) on the final

performance. Furthermore, the result of this analysis may help in determining the proper

detection threshold T to obtain a certain false positive rate. The authors in [35] claimed

that by abandoning the pure Gaussian noise assumption, some substantial performance

improvements could be obtained.

In [4], the authors embedded a watermark signal domain by modifying a number of

predefined DCT coefficients. They used a weighting factor to weight the watermark

signal in the spatial domain according to HVS characteristics. In [75] authors embedded

watermark data in DCT Difference (JND) as predicted domain in perceptually meaningful

way and used the Just Noticeable by model reported in [108].

2.2.2.1 THE MIDDLE-BAND COEFFICIENT EXCHANGE SCHEME [42, 52]:

The middle-band frequencies (FM) of an 8x8 DCT block are shown in Figure 2.3. In this

Figure, FL is used to denote the lower frequency components of the block and FH is used

to denote the higher frequency components. FM is chosen as embedding region to

provide additional resistance to lossy compression techniques, while avoiding significant

modification of the cover image. First, 8x8 DCT of an original image is taken. Then, two

locations DCT (u1, v1) and DCT (u2, v2) are chosen from the FM region for comparison of

each 8 x 8 block. These locations are selected based on the recommended JPEG

quantization table shown in Figure 2.4. If two locations are chosen such that they have

identical quantization values, then any scaling of one coefficient will scale the other by

the same factor to preserve their relative strength. It may be observed from Figure 2.4,

that coefficients at location (4, 1) and (3, 2) or (1, 2) and (3, 0) are more suitable

candidates for comparison because their quantization values are equal. The DCT block


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will encode a “1” if DCT (u1, v1) > DCT (u2, v2); otherwise it will encode a “0”. The

coefficients are swapped if the relative size of coefficients does not agree with the bit that

is to be encoded [42, 52].

Thus, instead of embedding any data, this scheme is hiding watermark data by means of

interpreting “0” or “1” with relative values of two fixed locations in middle frequency

region.

FL

FM

FH

Figure 2.3: Frequency regions in 8 x 8 DCT

Swapping of such coefficients will not alter the watermarked image significantly, as it is

generally believed that DCT coefficients of middle frequencies have similar magnitudes.

Further, the robustness of the watermark can be improved by introducing a watermark

“strength” constant k , such that DCT (u1, v1) – DCT (u2, v2) > k . If coefficients do not

meet these criteria, they are modified by the use of random noise to satisfy the relation.

Increasing k thus reduces the chance of detection errors at the expense of additional

image degradation. By increasing k, larger coefficients remain larger even after lot of

compression and thus help in decoding because their relative values decide the decodingof the watermark data.

While extracting the watermark, again the 8x8 DCT of image in taken in which “1” is

decoded if DCT (u1, v1) > DCT (u2, v2); otherwise a “0” is decoded.


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Figure 2.4: JPEG Quantization matrix

Limitation of middle-band coefficient exchange scheme: Experimental results show that

Middle-Band Coefficient Exchange is quite efficient against JPEG compression,

Cropping, Noising and other common image manipulation operations. But above scheme

has one serious drawback. If only one pair of coefficient is used (say (4, 1) and (3, 2)) tohide the watermark data, then it is vulnerable to collusion attack. By analyzing four or

five watermarked copies of an image, one can easily find out that these coefficients

always have a certain pattern and attacker can predict the watermark as well as destroy it.

2.2.2.2 DCT-CDMA BASED IMAGE WATERMARKING [42, 52]: In this

technique authors embedded a PN sequence W into the middle frequencies of the DCT

block. A DCT block can be modulated using the Equation 2.2.

∉

∈+=

M

M y x y x

y xW F vuvu y Ix

F vuvuW k vu I vu I

,),,(,

,),,(*),(),( ,,, ………………………………………….. (2.2)

For each 8 x 8 block of the image, the DCT for the block is first calculated. In that block,

the middle frequency components FM are added to the PN sequence W, multiplied by a

gain factor k. Each block is then inverse-transformed to give the final watermarked imageIW.

The watermarking procedure is made somewhat more adaptive by slightly altering the

embedding process to the method shown in Equation 2.3.


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∉

∈+=

M

M y x y x

y xW F vuvu y Ix

F vuvuW k vu I vu I

,),,(,

,)),,(*1(*),(),( ,,, ……………………………... (2.3)

This slight modification scales the strength of the watermarking based on the size of the

particular coefficients being used. Larger values of k can thus be used for coefficients of

higher magnitude; in effect strengthening the watermark in regions that can afford it;

weakening it in other regions.

For detection, the image is broken up into same 8x8 blocks and a DCT is taken. The same

PN sequence is then compared to the middle frequency values of the transformed block.

If the correlation between the sequences exceeds some threshold T, a “1” is detected for

that block; otherwise a “0” is detected. Again k denotes the strength of the watermarking,

where increasing k increases the robustness of the watermark at the expense of quality.

2.2.3 DWT BASED WATERMARKING SCHEMES

If watermarking techniques can exploit the characteristics of the Human Visual System

(HVS), it is possible to hide watermarks with more energy in an image, which makes

watermarks more robust. From this point of view, the DWT is a very attractive transform,

because it can be used as a computationally efficient version of the frequency models forthe HVS [5]. For instance, it appears that the human eye is less sensitive to noise in high

resolution DWT bands and in the DWT bands having an orientation of 45° (i.e., HH

bands). Furthermore, DWT image and video coding, such as embedded zero-tree

wavelet (EZW) coding, are included in the upcoming image and video compression

standards, such as JPEG2000 [112]. Thus DWT decomposition can be exploited to

make a real-time watermark application.

Many approaches apply the basic schemes described at the beginning of this section

to the high resolution DWT bands, LH , HH , and HL [35, 40]. A large number of

algorithms operating in the wavelet domain have been proposed till date.


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Figure 2.5: 1-Scale and 2-Scale 2-Dimensional Discrete Wavelet Transform

2.2.3.1 CDMA-DWT BASED WATERMARKING SCHEME: This scheme is the

most straightforward scheme which is similar to embedding scheme to that used in the

DCT-CDMA scheme. The embedding of a CDMA sequence in the frequency bands is

shown in Equation 2.4.

∈

∈+=

HH LLvuW

LH HLvu xW W I

i

iii

vuW ,,

,,,,

α

………………………………………………. (2.4)

where Wi denotes the coefficient of the transformed image, xi the bit of the watermark to

be embedded, and α a scaling factor. To detect the watermark, same pseudo-randomsequence used in CDMA generation is generated and its correlation is determined with

the two transformed frequency bands. If the correlation exceeds some threshold T, the

watermark is detected.

This can be easily extended to multiple bit messages by embedding multiple watermarks

into the image. In the spatial version, a separate seed is used for each PN sequence,

which are then added to the frequency coefficients. During detection, if the correlationexceeds T for a particular sequence a “1” is recovered; otherwise a “0”. The recovery

process then iterates through the entire PN sequence until all the bits of the watermark

have been recovered.


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DWT based watermarking schemes follow the same guidelines as DCT based schemes,

i.e. the underlying concept is the same; however, the process to transform the image into

its transform domain varies and hence the resulting coefficients are different. Wavelet

transforms use wavelet filters to transform the image. There are many available filters,

although the most commonly used filters for watermarking are Haar Wavelet Filter,

Daubechies Orthogonal Filters and Daubechies Bi-Orthogonal Filters. Each of these

filters decomposes the image into several frequencies. Single level decomposition gives

four frequency representations of the images. In their paper [76], authors presented a

survey of wavelet based watermarking algorithms. They classify algorithms based on

decoder requirements as Blind Detection or Non-blind Detection. As mentioned earlier

blind detection doesn't require the original image for detecting the watermarks; however,

non-blind detection requires the original image.

2.2.3.2 DWT BASED BLIND WATERMARK DETECTION: Lu et al. [58]

presented a novel watermarking technique called as "Cocktail Watermarking". This

technique embeds dual watermarks which compliment each other. This scheme is

resistant to several attacks, and no matter what type of attack is applied; one of the

watermarks can be detected. Furthermore, they enhance this technique for image

authentication and protection by using the wavelet based Just Noticeable Distortion

(JND) values. Hence this technique achieves copyright protection as well as content

authentication simultaneously. Zhu et al. [126] presented a multi-resolution watermarking

scheme for watermarking video and images. The watermark is embedded in all the high

pass bands in a nested manner at multiple resolutions. This scheme doesn't consider the

HVS aspect; however, Kaewkamnerd and Rao [43-44] improve this scheme by adding

the HVS factor in account. Voyatzis and Pitas [104], who presented the "toral

automorphism" concept, provide a technique to embed binary logo as a watermark which

can be detected using visual models as well as by statistical means. So, in case the image

is degraded too much and the logo is not visible, it can be detected statistically using

correlation. Watermark embedding is based on a chaotic (mixing) system. Original image

is not required for watermark detection. However, the watermark is embedded in spatial

domain by modifying the pixel or luminance values.


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A similar approach is presented for the wavelet domain [121], where the authors

proposed a watermarking algorithm based on chaotic encryption. Zhao et al.[125]

presented a dual domain watermarking technique for image authentication and image

compression. They used the DCT domain for watermark generation and DWT domain for

watermark insertion. A soft authentication watermark is used for tamper detection and

authentication while a chrominance watermark is added to enhance compression. They

use the orthogonality of DCT-DWT domain for watermarking [125].

2.2.3.3 DWT BASED NON-BLIND WATERMARK DETECTION: This technique

requires the original image for detecting the watermark. Most of the schemes found in

literature use a smaller image as a watermark and hence cannot use correlation based

detectors for detecting the watermark; as a result they rely on the original image forinformed detection. The size of the watermark image (normally a logo) normally is

smaller compared to the host image. Xia et al. presented a wavelet based non-blind

watermarking technique for still images where watermarks are added to all bands except

the approximation band. A multi-resolution based approach with binary watermarks is

presented here [37]. Here, both the watermark logo as well as the host image isdecomposed into sub bands and later embedded. Watermark is subjectively detected by

visual inspection; however, an objective detection is employed by using normalized

correlation. Lu et al. presented another robust watermarking technique based on image

fusion. They embedded a grayscale and binary watermark which is modulated using the

"toral automorphism" described in [106]. Watermark is embedded additively. The

novelty of this technique lies in the use of secret image instead of host image for

watermark extraction and use of image dependent and image independent permutations to

de-correlate the watermark logos [57]. Raval and Rege presented a multiple

watermarking scheme. The authors argued that if the watermark is embedded in the low

frequency components, it is robust against low pass filtering, lossy compression and

geometric distortions. On the other hand, if the watermark is embedded in high frequency

components, it is robust against contrast and brightness adjustment, gamma correction,

histogram equalization and cropping and vice-versa. Thus, to achieve overall robustness


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against a large number of attacks, the authors proposed to embed multiple watermarks in

low frequency and high frequency bands of DWT [78].

Kundur and Hatzinakos [50] presented image fusion watermarking scheme. They usedsalient features of the image to embed the watermark. They used a saliency measure to

identify the watermark strength and later embedded the watermark additively.

Normalized correlation is used to evaluate the robustness of the extracted watermark.

Later the authors proposed another scheme termed as FuseMark [51] which includes

minimum variance fusion for watermark extraction. Here, they propose to use a

watermark image whose size is a factor of the host by 2xy. Tao and Eskicioglu presented

an optimal wavelet based watermarking scheme. They embedded binary logo watermark

in all the four bands. But they embedded the watermarks with variable scaling factor indifferent bands. The scaling factor is high for the LL sub band but for the other three

bands it is lower. The quality of the extracted watermark is determined by Similarity

Ratio measurement for objective calculation [100]. Ganic and Eskicioglu inspired by

Raval and Rege [78] proposed a multiple watermarking scheme based on DWT and

Singular Value Decomposition (SVD). They argued that the watermark embedded by

Raval and Rege [78] scheme was visible in some parts of the image especially in the low

frequency areas, which reduced the commercial value of the image. Hence they

generalized their scheme by using all the four sub bands and embedding the watermark in

SVD domain. The core technique is to decompose an image into four sub bands and then

applying SVD to each band. The watermark is actually embedded by modifying the

singular values from SVD [30].

2.3 RECENT METHODOLOGIES

Now-a-days, researchers are focusing on mixing of spatial and transformed domains (i.e.

combinations of DFT, DWT and DCT) concepts and also applying more and more

mathematical and statistical model, and other interdisciplinary approaches in

watermarking: for example use of chaotic theory, fractal image coding etc. In this section

we are presenting the brief of few recent watermarking algorithms.


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In [103], authors presented a reversible watermarking scheme for the 2D-vector data

(point coordinates), which are used in geographical information related applications. This

reversible watermarking scheme exploits the high correlation among points in the same

polygon in a map and achieves the reversibility of the whole scheme by an 8-point

integer DCT, which ensures that the original 2D-vector data can be watermarked during

the watermark embedding process and then perfectly restored during the watermark

extraction process. In this scheme, author used an efficient highest frequency coefficient

modification technique in the integer DCT domain to modulate the watermark bit “0” or

“1”, which can be determined during extraction without using any additional information.

To alleviate the visual distortion in the watermarked map caused by the coefficient

modification, they proposed an improved reversible watermarking scheme based on theoriginal coefficient modification technique. Combined with this improved scheme, the

embedding capacity could be greatly increased while the watermarking distortion is

reduced as compared to the original coefficient modification scheme presented in [103].

In [65], authors presented zero-knowledge watermark detectors. Current detectors are

based on a linear correlation between the asset features and a given secret sequence. This

detection function is susceptible of being attacked by sensitivity attacks for which zero-

knowledge does not provide protection. In this work, a new zero-knowledge watermark

detector robust to sensitivity attacks is presented, using the generalized Gaussian

Maximum Likelihood (ML) detector as the basis. The inherent robustness that this

detector presents against sensitivity attacks, together with the security provided by the

zero-knowledge protocol that conceals the keys that could be used to remove the

watermark or to produce forged assets, results in a robust and secure protocol.

Additionally, two new zero-knowledge proofs for modulus and square root calculation

are presented. They serve as building blocks for the zero-knowledge implementation of

the Generalized Gaussian ML detector, and also open new possibilities in the design of

high level protocols.


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If digital watermarking is to adequately protect content in systems which provide

resolution and quality scalability, then the watermarking algorithms must provide both

resolution and quality scalability. Although there exists a trade off between resolution

and quality scalability, it has been demonstrated that it is possible to achieve both types

by taking advantage of human visual system characteristics to increase quality scalability

without compromising resolution scalability. Watermarking algorithms considering this

problem have been proposed; however, they tend to focus on a single type of scalability,

resolution [96, 120] or quality [12, 98]. Peng et al. [66] considered both types, but their

algorithm deals exclusively with authentication and is not a watermarking algorithm. In

their work [67] authors focused on providing a spread spectrum watermarking algorithm

which had both resolution and quality scalability demonstrated through experimental

testing using the JPEG2000 compression algorithm. To alleviate this trade off, they beganwith a non-adaptive resolution scalable algorithm and exploited the contrast sensitivity

and texture masking characteristics of the HVS to construct an HVS adaptive algorithm

that has good quality scalability. Their algorithm is specifically designed to concentrate

on textured regions only, avoiding the visible distortions, which may occur when strength

increases are applied to edges. Furthermore, this texture algorithm is applied in the

wavelet domain but uses only a single resolution for each coefficient to be watermarked.

In their work [126], authors presented a new image adaptive watermarking scheme based

on perceptually shaping watermark block wise. Instead of the global gain factor, a

localized one is used for each block. Watson’s DCT-based visual [109] model is adopted

to measure the distortion of each block introduced by watermark, rather than the whole

image. With the given distortion constraint, the maximum output value of linear

correlation detector is derived in one block, which demonstrated the reachable maximum

robustness in a sense. Meanwhile, an EXtended Perceptually Shaped Watermarking (EX-

PSW) is acquired through making detection value which approaches to upper limit. It is

proved mathematically that EX-PSW outputs higher detection value than Perceptually

Shaped Watermarking (PSW) with the same distortion constraint. Authors used this idea

and also discussed the adjustment strategies of parameters in EX-PSW, which were


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helpful for improving the local image quality. Experimental results show that scheme

provides very good results both in terms of image transparency and robustness.

In [10], authors presented an Independent Component Analysis (ICA) [40-41] based

watermarking method. This watermarking scheme is domain-independent ICA-based

approach. This approach can be used on images, music or video to embed either a robust

or fragile watermark. In the case of robust watermarking, the method shows high

information rate and robustness against malicious and non-malicious attacks while

inducing low distortion. Another version of this scheme is a fragile watermarking scheme

which shows high sensitivity to tampering attempts while keeping the requirement for

high information rate and low distortion. The improved performance is achieved by

employing a set of statistically independent sources (the independent components) as thefeature sp

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