IMAGE WATERMARKING OPTIMIZATION ALGORITHMS IN TRANSFORM

DOMAINS AND FEATURE REGIONS

HAI TAO

Thesis submitted in fulfilment of the requirements of the award of the degree of

Doctor of Philosophy (Computer Science)

Faculty of Computer Systems and Software Engineering

UNIVERSITI MALAYSIA PAHANG

April 2012

vi

ABSTRACT

Digital watermarking techniques have been explored considerably since its first

appearance in the 1990s. The achieved tradeoffs from these techniques between

imperceptibility and robustness are controversial. To solve this problem, this study

proposes the application of artificial intelligent techniques into digital watermarking

by using discrete wavelet transform (DWT) and singular value decomposition (SVD).

To protect the copyright information of digital images, the original image is

decomposed according to two-dimensional discrete wavelet transform. Subsequently

the preprocessed watermark with an affined scrambling transform is embedded into

the vertical subband (HLm) coefficients in wavelet domain without compromising the

quality of the image. The scaling factors are trained with the assistance of Particle

Swarm Optimization (PSO). A new algorithmic framework is used to forecast

feasibility of hypothesized watermarked images. In addition, the novelty is to

associate the Hybrid Particle Swarm Optimization (HPSO), instead of a single

optimization, as a model with SVD. To embed and extract the watermark, the singular

values of the blocked host image are modified according to the watermark and scaling

factors. A series of training patterns are constructed by employing between two

images. Moreover, the work takes accomplishing maximum robustness and

transparency into consideration. HPSO method is used to estimate the multiple

parameters involved in the model. Unfortunately, watermark resistance to geometric

attacks is the most challenge work in traditional digital image watermarking

techniques which causes incorrect watermark detection and extraction. Recently, the

strategy of researchers has introduced image watermarking techniques using the

invariant transforms for their rotation and scale invariant properties. However, it

suffers from local transformations which make watermarks difficult to recover. This

thesis will introduce a set of content based image watermarking schemes which can

resist both local geometric attacks and traditional signal processing attacks

simultaneously. These schemes follow a uniform framework, which is based on the

detection of feature points which are commonly invariant to Rotation, Scaling and

Translation (RST), therefore they naturally accommodate the framework of

geometrically robust image watermarking. As a result, it will first introduce the

theories about the feature extraction and the basic principles on how feature points can

act as locating resynchronization between watermark insertion and extraction

discussed in detail. Subsequently, it will present several content-based watermark

embedding and extraction methods which can be directly implemented based on the

synchronization scheme. Further detailed watermarking schemes which combine

feature regions extraction with counter propagation neural network-based watermarks

synapses memorization are then presented. The performance of watermarking

schemes based on framework of feature point shows the following advantages: (a)

Good imperceptibility. It is obvious that the watermarking schemes show a little

influence on watermark invisibility; (b) Good robustness. The proposed scheme is not

only robust against common image processing operations as sharpening, noise adding,

and JPEG compression etc, but also robust against the desynchronization attacks such

as rotation, translation, scaling, row or column removal, cropping, and local random

bend etc.

vii

ABSTRAK

Teknik watermarking digital telah diterokai sejak kemunculan pertamanya pada tahun

1990-an. Pengimbangan yang dicapai ini menjadi kontroversi antara kebolehlihatan

dan keteguhan teknik-teknik tersebut. Untuk menyelesaikan masalah ini, kajian ini

mencadangkan teknik kepintaran buatan ke dalam watermarking digital dengan

menggunakan diskret koncah mengubah (DWT) dan penguraian nilai singular (SVD).

Untuk melindungi maklumat hak cipta imej digital, imej asal terurai mengikut koncah

diskret mengubah dua dimensi. Seterusnya pra proses watermarking dengan

mengubah affined rawak yang tertanam di dalam subband menegak (HLm) pekali

dalam domain ombak kecil tanpa menjejaskan kualiti imej. Faktor-faktor scaling

dilatih dengan bantuan Particle Swarm Optimization (PSO). Satu rangka kerja

algoritma baru digunakan untuk meramal kemungkinan imej hipotesis watermarking.

Di samping itu, pembaharuan ini adalah untuk mengaitkan Hybrid Particle Swarm

Optimization (HPSO), dan bukannya pengoptimuman satu, sebagai satu model dengan

SVD. Untuk menerapkan dan cabutan watermarking, nilai-nilai tunggal imej tuan

rumah yang telah disekat telah diubah suai mengikut watermarking dan faktor-faktor

penskalaan. Satu siri corak latihan yang dibina dengan menggunakan antara dua imej.

Selain itu, penyelidikan ini mencapai tahap keteguhan maksimum dan kebolehlihatan.

Kaedah HPSO digunakan untuk menganggar parameter berbilang yang terlibat dalam

model. Malangnya, rintangan watermarking yang berupa serangan geometri di dalam

teknik tradisional watermarking imej digital yang menyebabkan pengesanan dan

pengestrakan watermarking salah. Strategi penyelidik semasa telah memperkenalkan

teknik imej watermarking yang menggunakan perubahan yang tidak berubah kepada

ciri-ciri putaran dan skala tak berubah. Walau bagaimanapun, ia mengalami

transformasi tempatan yang membuatkan watermarking susah untuk pulih. Tesis ini

akan memperkenalkan satu set imej kandungan berasaskan skim watermarking yang

boleh melawan kedua-dua serangan geometri dan serangan pemprosesan isyarat

tradisional secara serentak. Skim ini mengikut satu rangka kerja yang seragam, yang

berasaskan pengesanan ciri-ciri penting dimana kebiasaannya tidak membawa

perubahan kepada putaran, penskalaan dan penterjemahan, maka mereka secara

semulajadi akan menampung rangka kerja geometri watermarking imej teguh. Oleh

itu, ia akan memperkenalkan teori-teori tentang pengekstrakan ciri dan prinsip-prinsip

asas bagaimana ciri-ciri penting boleh bertindak sebagai penempatan penyelarasan

semula antara penyisipan dan pengekstrakan watermarking dibincangkan secara

terperinci. Kemudian, ia akan menghuraikan beberapa kaedah pembenaman dan

pengekstrakan watermarking muatan asas yang boleh terus dilaksanakan berdasarkan

skim penyelarasan. penerangan selanjutnya mengenai skim watermarking yang

menggabungkan pengekstrakan ciri kawasan dengan rangkaian neural perambatan

kaunter berasaskan watermarking sinaps hafalan kemudiannya dibentangkan. Prestasi

skim watermarking yang berdasarkan rngka kerja ciri penting menunjukkan kelebihan

berikut: (a) Baik dalam keadaan tidak ketara. Ia jelas bahawa bahawa skim

watermarking menunjukkan pengaruh yang kecil ke atas keadaan tidak ketara

watermarking; (b) Keteguhan yang baik. Skim yang dicadangkan bukan sajaha teguh

terhadap operasi pemprosessan imej biasa sebagai penajam, penambahan bunyi bising,

pemampatan JPEG dan sebagainya, tetapi juga teguh terhadap serangan penyelarasan

semula seperti putaran, penterjemahan, penskalaan, penyingkiran lajur dan baris,

pemotongan, pembengkokkan rawak tempatan dan lain-lain.

viii

TABLE OF CONTENTS

Page

SUPERVISOR’S DECLARATION ii

STUDENT’S DECLARATION iii

DEDICATION iv

ACKNOWLEDGEMENTS v

ABSTRACT vi

ABSTRAK vii

TABLE OF CONTENTS viii

LIST OF TABLES xii

LIST OF FIGURES xiii

LIST OF NOMENCLATURES xv

LIST OF ABBREVIATIONS xvi

CHAPTER 1 INTRODUCTION

1.1 Background 1

1.2 Definition of Watermarking 2

1.3 Problem Statements 4

1.4 Research Objectives 6

1.5 Research Contributions 7

1.6 Scope of the Thesis 8

1.7 Thesis Organization 9

CHAPTER 2 BACKGROUNDS AND LITERATURE REVIEW

2.1 Introduction 10

2.2 History of Watermarking 11

2.3 Watermarking Background 13

2.4 Image Watermarking Framework 15

2.4.1 Encoder 16

2.4.2 Detector 16

2.5 Characteristics of Watermarking Systems 17

ix

2.5.1 Imperceptibility 17

2.5.2 Robustness 17

2.5.3 Capacity 18

2.5.4 Security 19

2.5.5 False Positive 19

2.6 Classification Of Digital Watermarking

Applications

20

2.6.1 Digital Watermarking For Copyright

Protection

20

2.6.2 Fingerprinting 21

2.6.3 Copy Control 21

2.7 Attacks Operators 22

2.7.1 Removal Attacks 23

2.7.1.1 Denoising And Lossy

Compression Attacks

23

2.7.1.2 Remodulation Attacks 23

2.7.1.3 Averaging And Collusion Attacks 24

2.7.2 Geometrical Attacks 25

2.7.3 Cryptographic Attacks 26

2.7.4 Protocol Attacks 26

2.7.5 Image Shifting And Line Deletion 26

2.8 Geometric Invariant Watermarking 27

2.8.1 Exhaustive Watermark Search 27

2.8.2 Template-Based Approach and Self-

Synchronizing

28

2.8.3 Invariant Domain 29

2.8.4 Feature Watermarking 30

2.9 Summary 33

CHAPTER 3 METHODOLOGIES

3.1 Introduction 34

3.2 Frequency Domain Transform 34

3.2.1 Singular Value Decomposition 35

3.2.2 Discrete Wavelet Transform 36

3.3 Corner Points Detection 37

3.3.1 Harris Corner Detectors 39

3.3.2 Harris Corner Detection in Scale–Space 42

3.3.3 Harris–Laplace Detector 43

3.3.4 Maximum Features Selection 44

3.3.5 Affine Invariant Detectors 45

3.3.6 Selecting Affine-Harris Detector 47

3.3.7 Affine Second Moment Matrix 48

x

3.3.8 Affine Transformation of Second Moment

Matrix

49

3.3.9 Isotropy Measure 51

3.4 Artificial Intelligence Algorithms 52

3.4.1 Particle Swarm Optimization 53

3.4.2 Hybrid Particle Swarm Optimization 54

3.4.3 Counter Propagation Neural Network 55

3.5 Summary 60

CHAPTER 4 WATERMARKING ALGORITHMS IN TRANSFORM

DOMAIM

4.1 Introduction 62

4.2 Singular Value Decomposition Based on Hybrid

Particle Swarm Optimization

62

4.2.1 Watermark Preprocessing 65

4.2.2 Watermark Embedding Scheme 66

4.2.3 Watermarking Extracting Scheme 66

4.2.4 Parameters setting 67

4.2.5 Experimental results 70

4.2.6 Experimental Results of Imperceptibility 73

4.2.7 Experimental Results of Robustness 74

4.2.8 Computational Efforts Reflecting 78

4.3 Particle Swarm Optimization And Discrete Wavelet

Transform Applied To Watermarking

78

4.3.1 Proposed Scheme using DWT and PSO 80

4.3.2 Watermark Embedding using DWT and

PSO

81

4.3.3 Watermark Extraction using DWT and

PSO

82

4.3.4 Proposed Optimization Process using

DWT and PSO

83

4.3.5 Experimental Results 89

4.3.6 Imperceptibility Using DWT and PSO 89

4.3.7 Robustness Using DWT and PSO 90

4.4 Conclusions 91

CHAPTER 5 WATERMARKING ALGORITHMS IN FEATURE DOMAIN

5.1 Introduction 93

xi

5.2 Image Normalization 94

5.3 Invariant Watermarking in Local Circular Regions 95

5.3.1 Feature Regions Extraction By Harris-

Laplace Detectors

95

5.3.2 Synchronization In Circular Regions 97

5.3.3 Proposed Watermarking Scheme In Local

Circular Regions

99

5.3.4 Watermark Embedding in Local Circular

Regions

100

5.3.5 Watermark Detecting in Local Circular

Regions

101

5.3.6 Experimental Results in Local Circular

Regions

102

5.3.7 Imperceptibility Tests in Local Circular

Regions

102

5.3.8 Robustness Tests in Local Circular Regions 104

5.4 Invariant Watermarking in Affine Circular Regions 105

5.4.1 Feature Regions Extraction By Harris-

Affine Detectors

106

5.4.2 Synchronization in Affine Circular Regions 107

5.4.3 Proposed Watermarking Scheme in Affine

Circular Regions

110

5.4.4 Watermark Embedding in Affine Circular

Regions

111

5.4.5 Watermark Detecting in Affine Circular

Regions

112

5.4.6 Experiments in Affine Circular Regions 113

5.4.7 Imperceptibility Test in Affine Circular

Regions

114

5.4.8 Robustness Test in Affine Circular Regions 114

5.5 Gray-Level Watermarks Insertion and Extraction 116

5.5.1 Grouping Selection 117

5.5.2 Proposed Watermarking Scheme Using

Gray-Level Watermarks

120

5.5.3 Watermarks embedding Using Gray-Level

Watermarks 119

5.5.4 Watermarks extracting Using Gray-Level

Watermarks 121

5.5.5 Imperceptibility Test for Gray-Level

Watermarks

122

5.5.6 Robustness Test for Gray-Level

Watermarks 123

5.6 Summary 127

CHAPTER 6 CONCLUSION AND RECOMMENDATIONS

xii

6.1 Introduction 129

6.2 Contributions 129

6.3 Conclusions 131

6.4 Opportunities for Further Research 132

6.4.1 Feature Theory and Digital Watermarking 132

6.4.2 E-Government Security 133

6.4.3 Smartphone Application and Development 134

6.4.3.1 Launch Multimedia Contents 134

6.4.3.2 Smartphone Wallet Application 135

REFERENCES

APPENDICES

A The summary of literature review

LIST OF PUBLICATIONS

137

148

154

xiii

LIST OF TABLES

Table No. Title Page

4.1 NC value obtained by scaling factors (SF) of the three

images

75

4.2 Fitness values for different population sizes (PS) 75

4.3 The NC values comparison of extracted watermarks with

existing schemes

90

5.1 Watermark Detection Results under General Image

Operations

103

5.2 Watermark Detection Results under Geometric Distortions 104

5.3 Watermark Detection Results under General Image

Operations

116

5.4 Watermark Detection Results under Geometric Distortions 116

xiv

LIST OF FIGURES

Figure No. Title Page

2.1 Nonblind watermarking framework 15

2.2 Blind watermarking framework 16

3.1 (a) Three-level wavelet transform of the „Lena‟ image 37

3.1 (b) three-level wavelet transform of the image 37

3.2 The relationship between eigenvalues and image structure 41

3.3 Structure of counter propagation neural network 56

3.4 The structure of the thesis 61

4.1 The Proposed Method (Watermarking Based On HPSO) 71

4.2 The original image: (a) Lena. (b) Barbara. (c) Mandrill. and the

original watermark (d)

72

4.3 The imperceptibility comparison of watermarked images 73

4.4 Experiment results of the attacked watermarked images and

extracted watermarks using (a) Rotation (RT) (in degree 10) (b)

Resize (RS) (100% to 50% to 100%) (c) Dithering (DT) (d)

Gaussian noise (GN) (0, 0.01) (e) Tampering (TP) (f) Cropping

(CP)(10%) (g) JPEG compression (QF 40) (h) Low pass filtering

(LP) (33) (i) Salt and pepper noise (SP) (0, 0.01)

76

4.5 Fitness values for different population sizes 77

4.6 Diagram for proposed scheme 85

4.7 The original images of (a) Lena; (b) Baboon; (c) watermark and

(d) scrambled watermark

86

4.8 The watermarked images of (a) Lena and (b) Baboon in the

proposed scheme and the watermarked images of (c) Lena and

(d) Baboon in Hsieh scheme

86

4.9 Several attacks on “Lena” and “Baboon” images and their

corresponding extracted watermarks

88

5.1 Illustration of selection local circle regions in Baboon, Lena, and

Peppers images: (a) the original local circle regions, (b) the local

circle regions in middle scale band, and(c) the final chosen local

98

xv

circle regions

5.2 Partition the local circle region 99

5.3 (a) Original image, (b) watermarked image, and (c) difference

image in Lena and Peppers

103

5.4 Illustration of selection affine circle regions in Lena, Baboon,

and Peppers images: (a) the original affine circle regions, (b) the

affine circle regions in middle scale band, and(c) the final chosen

affine circle regions.

108

5.5 Partition the affine circle region 109

5.6 Rotation invariant of partition the normalized affine circle

regions

110

5.7 (a) Original image, (b) watermarked image, and (c) difference

image in Baboon and Lena

113

5.8 (a) Original image, (b) Watermarks embedded regions, and (c)

difference image watermarked image in Lena

122

5.9 (a) Original watermark, (b) Watermarks extracted from six

feature regions

123

5.10 Gray-level watermarks detection from attacked watermarking

image in Lena

127

xvi

LIST OF SYMBOLS

X Input Training Vectors X=(X1, X2,… Xn)

Y Input Training Watermarks Y= (Y1, Y2,… Ym)

D Neuron Vectors Of The Kohonen Layer D=( D1, D2,… Dl)

W Weight Interconnecting Between X Input Layer To

Neuron Unit Di W=( W1i, W2i,… Wni)

V Weight Interconnecting Between Y Input Layer To

Neuron Unit Di V=( V1i, V2i,… Vmi)

X* Output Vectors In The Grossberg Layer X=(X1, X2,… Xn)

Y* Output Watermarks In The Grossberg Layer Y= (Y1,

Y2,… Ym)

T Weight Interconnecting Between Host Image X Output

Layer To Ith Neuron Unit Di T=( T1i, T2i,… Tni)

U Weight Interconnecting Between Watermark Y Output

Layer To Ith Neuron Unit Di U=( U1i, U2i,… Umi)

( )k Learning Parameters During Kohonen Learning

( )k Learning Parameters During Grossberg Learning

xvii

LIST OF ABBREVIATIONS

ACR Affine circular regions

BCD Binary Coded Decimal

CR Cirque regions

LCR Local circular regions

LoG Laplacian-of- Gaussians

NFC Near field communication

SR Subregions

PDA Personal digital assistant

DVD Digital Versatile Disc

SVD Singular Value Decomposition

DWT Discrete Wavelet Transform

PNN Probabilistic Neural Network

AI Artificial Intelligence

NC Normalized Correlation

UQI Universal Quality Index

DCT Discrete Cosine Transform

CHAPTER 1

INTRODUCTION

1.1 BACKGROUND

Due to the rapid and comprehensive development of network technologies,

digital multimedia contents can be delivered with maintained good quality, almost

immediate transmission protection and low price of multimedia contents has recently

developed into a significant problem because of purchaser's insufficient recognition of

the possession of intellectual property. Therefore, over the past a couple of decades,

digital multimedia technology has turned up to search for responses to the question: can

researchers ensure tamper-resistance and protect the copyright of digital contents by

processing, communicating and storing digital information encoded in procedures

where digital media content be able to effortlessly be distributed through

communication channels? Today it is fully apprehended that the answer is positive, and

numerous researchers all over the world are studying towards the considerably

progressive technical target of protecting the ownership of digital contents. The efforts

would dramatically protect inventions represented in digital form for being vulnerable

to illegal possession, duplication and dissemination. Digital watermarking (Cox I.J.,

et.al. 1997) is the process of embedding or hiding digital information called watermark

into a multimedia product, and then the embedded data can later be extracted or

detected from the watermarked product. The technique can protect digital content

copyright and ensure tamper-resistance, which is indiscernible and hard to remove by

unauthorized persons.

Up to now, two traditionally-used strategies, spatial-domain (Takahashi A., et al.

2005) and transform domain (Kim T.Y., et al. 2004) techniques have been developed

2

for digital image watermarking. The former category is designed to insert directly a

watermark into the original image by a factor, which would lead to fair-quality

watermarked images. The latter approach, for taking advantage of perceptual properties,

is devised to embed a watermark into transform domains of the original images. The

types watermarking schemes have good performances of robustness to the most

common signal processing manipulations such as JPEG compression, filtering, and

addition of noise (Cox I.J., et al. 1997; Cox I.J., et al. 2001; Lu C.S., et al. 2000). The

common image processing operators are applied to watermarked images for removing

the watermark or decreasing its energy, and the result is that the extracted watermark is

unrecognizable or insufficient as the validate evidence.

Unfortunately, the ineffectiveness of existing traditional watermarking

algorithms is described by the robustness against unintentional or malicious geometric

attacks (Licks V. and Jordan R., 2005). Especially, geometrical attacks cause

synchronization errors between the original watermark and the extracted one through

the extraction procedure. In other words, the watermark still includes in watermarked

images, but its positions have been changed. Therefore, traditional watermarking

systems need to be extended for the resilience to watermarked data geometrical

modifications. In the developed frameworks, synchronization errors correction is now

possible. Besides facilitating more efficient copyrighted protection and robustness

against common image processing operators and desynchronization attacks, adaptation

of geometrically invariant image features can potentially offer a greater robust capacity

to detect watermarks without synchronization errors, especially when applied to survive

local distortions such as Random bending attacks. The development of such framework

is an essential starting point for organizations that wish to improve, or replace currently

existing watermarking algorithms based pixels, frequency or other transform

coefficients for watermarks embedding and to develop a set of means to establish and

maintain feature-based watermarking of geometric distortions correction.

1.2 DEFINITION OF WATERMARKING

A great number of multimedia information is now being generated, distributed

and stored with digital forms, Newspapers, and magazines. For example, these have

3

published online to provide real-time coverage of stories with high-quality audio, still

images, and even video sequences. The explosive accretion in using public networks

such as the Internet has further encouraged the online presence of publishers by

providing an inexpensive and quick way to distribute their work. However, the great

growth of digital media is not limited to news organizations. Commercial music may be

downloaded off of the Internet and purchased, stock photography vendors digitize and

sell photographs in electronic form, and Digital Versatile Disc (DVD) systems provide

movies with clear images and CD-quality sound. Unfortunately, media stored in digital

form are vulnerable in a number of ways. First of all, digital media may be redistributed

and copied simply, either legally or illegally, at low cost and with no loss of

information. In addition, today's fast computers allow digital media to be easily

manipulated, so it is possible to incorporate portions of a digital signal into one's own

work without regard for copyright restrictions placed upon the work.

Digital watermarking is considered as a partial solution to the problem of

protecting copyright ownership. Essentially, watermarking is defined as the process of

embedding sideband data directly into the samples of a digital audio, image, or video

signal. Sideband data is typically ―extra‖ information that must be transmitted along

with a digital signal, such as block headers or time synchronization markers. It is

important to realize that a watermark is not transmitted in addition to a digital signal,

but rather as an indispensable part of the signal samples. The value of watermarking

comes from the fact that regular sideband data may be modified or lost when the digital

signal is converted between formats, but the samples of the digital signal are typically

unchanged.

To elucidate this concept further, it is helpful to consider an analogy between

digital watermarks and paper watermarks. Watermarks have traditionally been used as a

form of authentication for paper currency and legal documents. A watermark is

embedded within the fibers of paper when it is first constructed, and it is essentially

invisible unless held up to viewed at a particular angle or a special light. More

importantly, a watermark is very difficult to remove without destroying the paper itself,

and it is not transferred if the paper is photocopied. The targets of digital watermarking

4

are similar, and it will be shown in the next section that digital watermarks require

similar properties.

Before the concept of watermarking can be explored further, three important

definitions must first be established. A host signal is a raw digital audio, image, or

video signal that will be used to contain a watermark. A watermark itself is loosely

defined as a set of data, usually in binary form, that will be stored or transmitted

through an original signal. The watermark may be as small as a single bit, or as large as

the number of samples in the host signal itself. It may be a copyright notice, a secret

message, or any other information. Watermarking is the process of embedding the

watermark within the host signal. Finally, a secret key may be necessary to embed a

watermark into a host signal, and it may be needed to extract the watermark data

afterwards (Cox et al., 1997).

1.3 PROBLEM STATEMENTS

Digital watermarking algorithms are effectively applied to associated broadcast

monitoring systems and copy control applications. In combination with digital rights

management systems, these techniques can solve the bottleneck of the intellectual

property dilemma in audio- and image-related business areas. In an early stage of

watermarking, users considered resisting to geometric attacks as an advantage of one

technique over alternative implementations. For feature-based watermarking schemes,

however, surviving geometric attacks is a precondition. The past 10 years have

witnessed a important advancement in people recognizing of geometrical attacks and

methods for surviving them. Some approach proposed particular types of attacks,

although just a few of watermarking methods, in fact, survived the huge set of potential

variations of combined approaches. Even afterwards, many researchers identifying of

these threats, which the attacks compel on the performance of existent and upcoming

methods are limited. It may just reflect the limitations of efficient benchmarking

procedures and theoretical grounds for evaluating them..

(Cox et al. 1997) proposed a spread spectrum watermarking scheme based on

discrete cosine transform (DCT). The scheme aims to hide Gaussian distributed noise-

5

style watermarks. But these noisy style watermarks may not offer any resistance for

compressing the image data or for low-pass filtering attacks. (Lee et al. 2006) presented

a Genetic Algorithm-Based watermarking algorithm in the discrete wavelet transform

domain. Wavelet-domain low-frequency region watermark insertion and genetic

algorithm-based watermark extraction are possible in the algorithm. However, the

major deficiency is the higher degree of visual distortion which is similar to the results

shown in (Hisashi I., 2000). Additionally, GA suffers from important limitations of

high computational costs which eventually results in low convergence speed. (Ziqiang

et al., 2007) integrated DWT with particle swarm optimization (PSO) for watermarking

digital images. The technique embeds watermark into coefficients of DMT that holds

value larger than some threshold value. Subsequently the extraction is performed

through PSO. However, an obvious limitation of their proposed method is that the

application of PSO was simply done for evaluating the feasibility of the extracted

watermarks.

Additionally, (Chung et al. 2007) gave a guideline to embed the watermark into

both U and V components of the images using SVD. However, it was important to

mention that the previously cited watermarking schemes based on singular vectors

cannot preserve the orthgonalization of the U and V matrices. (Aslantas V 2008, 2009)

presented two optimized watermarking scheme using genetic algorithm (GA) and

differential evolution algorithm to obtain multiple scaling factors for embedding

watermark based on SVD-based watermarking method of (Liu and Tan, 2002).

However, (Zhang and Li 2005) noted that the watermarking algorithm was

fundamentally flawed in that the extracted watermark was not the embedded one but

decided by the reference watermark. The reference watermark generated the pair of

SVD matrices used in the watermark detector. In the watermark detection process, the

fact that the exploited SVD matrices relied on the reference watermark biased the false

positive detection rate such that it had a probability of one. Therefore, any reference

watermark that is being explored in an arbitrary image can be detected. However, less

research work has been reported so far on the corrected optimal SVD-based image

watermarking scheme.

6

Furthermore, while the common image processing operations reduce watermark

energy, desynchronization attacks induce synchronization errors between the original

and the extracted watermark during the detection process. Most of the previous

watermarking schemes are robust to common image processing operations, but show

severe problems to de-synchronization attacks. Today, most watermarking methods

cannot reach the main approach which is efficiently insensitive to various attacks. It is

still a wide and attractive field for further research in which innovative methods and

techniques may be established. In this thesis, it propose geometrically invariant digital

image watermarking approaches to construct watermark synchronization using neural

networks and local feature detectors to achieve its goals.

Watermark resistance to geometric attacks is the most challenge work in

traditional digital image watermarking techniques which causes incorrect watermark

detection and extraction. Recently, the strategy of researchers has introduced image

watermarking techniques using the invariant transforms for their rotation and scale

invariant properties. However, it suffers from local transformations which make

watermarks difficult to recover. For solving these problems, researchers proposed a

series of feature-based geometrically invariant watermarking algorithms simplifying to

synchronization deformation. Basically, any given watermarking system should be able

to have good performance of robustness, fidelity and data payload. Robustness ensures

that the retriever is still able to extract the watermark information even if the

watermarked image is deformed by various attacks. Fidelity deals with whether a

watermark was inappropriately embedded according to the perceptual between the

original and watermarked image. Finally, payload refers to the amount of efficient bits

that can be embedded in original images and retrieved reliably under normal operations.

1.4 RESEARCH OBJECTIVES

To acquire accomplishing maximum robustness and transparency into

consideration in transform domain.

To obtain the highest possible level of robustness while maintain the perceptual

transparency in feature regions.

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1.5 RESEARCH CONTRIBUTIONS

This work provides the following set of original contributions:

In transform domain, the corrected SVD-based optimal watermarking technique

is proposed for ownership protection based on hybrid particle swarm optimization

(HPSO). Experimental results show both the significant improvement in transparency

and the robustness under attacks. Simulation results demonstrate that the proposed

scheme can effectively improve the quality of the watermarked image and resist to

common image manipulations such as adding noise, resizing compression, tempering,

etc. and some geometric attacks.

In addition, the algorithm is devoted to introduce a novel algorithmic framework

for solving an optimal embedding and extracting problem in DWT domain. In the

proposed technique, the retrieval of watermark is distinguished with three important

features: i) it does not depend upon the availability of the original image; ii) it makes an

effective use of PSO for parameters estimation; iii) it possesses an appreciative

capability for simultaneously optimizing multiple scaling factors for obtaining the

highest possible level of robustness while maintaining the perceptual transparency in

embedding watermark images.

In feature regions, the algorithm is devoted to propose a novel geometrically

invariant watermarking method for solving an optimal embedding and extracting

problem using Harris-Laplace operators and counter propagation neural networks.

Sufficient experiments demonstrate the better robustness of this approach than others‘

scheme in the same level of imperceptibility, especially in resisting geometry attacks

and combined attacks.

In addition, the second method first proposes a geometrically invariant digital

image watermarking technique to construct watermark synchronization using synapses

memorization and affine covariant region. The comparison of the presented approach

with previous algorithms in terms of robustness and imperceptibility is also provided.

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The experimental results show that the proposed scheme has the remarkable

performance in resistant to common image processing operations survival the

desynchronization attacks.

Finally, to design gray-level watermark extraction scheme against

desynchronization attacks, the third method proposed a novel geometrically invariant

watermarking scheme based on grouping Harris-Laplace detector and the counter

propagation neural network with low computational complexity, good visual quality

and reasonable resistance toward desynchronization attacks. In the simulation results, it

demonstrates that the proposed image watermarking is not only imperceptibility and

robust against common image processing operations such as sharpening, noise adding,

and JPEG compression etc, but also robust against the geometric distortions such as

rotation, translation, scaling, row or column removal, cropping, and local random bend

etc.

1.6 SCOPE OF THE THESIS

To devoted to introduce a novel algorithmic framework for solving an optimal

embedding and extracting problem. The optimal algorithm achieves this by using a

forecasted feasibility for parameters evaluation in DWT domain.

To correct and propose SVD-based optimal watermarking technique for

ownership protection based on hybrid particle swarm optimization (HPSO). The

singular values of the host image are modified by embedding the watermark into the

blocked host images according to employing multiple scaling factors. The HPSO,

individuals and new generation are created, not only by crossover and mutation

operators as in GA, but also by PSO.

To develop a feature-based watermarking framework for attempting to reacquire

synchronization, allowing for complicated tradeoff between the three aforementioned

properties. To do so, the next part first investigates and develops feature selection

procedures which can be employed as a set of nonoverlapped and stable regions within

the framework including: Harris–Laplace detector and affine covariant region detectors

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and other improved feature detectors. Specifically, the counter propagation neural

network is applied to watermarks detection and extraction. It is not so much a new

discovery as it is a novel combination of previously existing network types. Hecht-

Nielsen synthesized the architecture from a combination of a structure known as a

competitive network and Grossberg structure. The operation of the network is quite

straightforward. The existence of a reset mechanism will remove the current input

pattern if another is stored. And this storage effect appears to be permanent. This long-

term memory behavior applies the efficient evidence for extracting watermarks from

seriously distorted watermarking images. And it handles low computational complexity,

good visual quality and reasonable resistance toward desynchronization attacks, in

particular the issues of bit error probability.

1.7 THESIS ORGANIZATION

The remainder of this dissertation is organized as follows. Chapter 2 provides a

summary of related work regarding the typical framework of image watermarking,

characteristics, applications and attacked techniques of watermarking systems, and

existing geometrically invariant watermarking. Chapter 3 describes specific

methodologies in the areas of transform domains images feature selection and artificial

intelligent techniques. Chapter 4 presented an optimal robust image watermarking

technique based on Discrete Wavelet Transform (DWT), and presents a novel optimal

robust image watermarking technique based on HPSO which is robust against a variety

of common image-processing attacks and some geometric attacks. Chapter 5 provides

three algorithms which the watermarks are embedded in the synapses of counter

propagation Neural Network. Experiments demonstrates that the proposed image

watermarking framework is not only imperceptibility and robust against common image

processing operations such as sharpening, noise adding, and JPEG compression etc, but

also robust against the geometric distortions such as rotation, translation, scaling, row

or column removal, cropping, and local random bend etc. Finally, Chapter 6 concludes

the work by providing a summary of the accomplishments as well as future directions

for research.

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CHAPTER 2

BACKGROUNDS AND LITERATURE REVIEW

2.1 INTRODUCTION

In this chapter reviews the appropriate background literature and describes the

concept of watermarking, characteristics of watermarking systems, the classification of

digital watermarking applications and attacked operators. Scientific publications

included into the literature survey have been chosen in order to build a sufficient

background that would help out in solving the research problems stated in Chapter 1. In

addition, Chapter 2 presents general concepts and definitions used and developed in

more details in Chapters 3, 4 and 5. The thesis is decided to divide the feature point

theoretical background, methodologies and experiments into three parts, presented in

Chapters 3, 4 and 5 because of the specific structure of the thesis, which presents three

different algorithms for geometric invariant image watermarking, contrary to the usual

schemes with the valued-added cover images in the special regions. Therefore, the

theoretical background and literature review in subjunction to the particular concept is

given as a separate chapter in the respective chapters. In this manner, it much easier for

the reader to follow the presented concepts, and the chapters themselves can also be

read as standalone readings.

In the first section, the properties of the general watermarking frameworks that

are exploited in the process of encoding and detecting watermarking are shortly

reviewed. A survey of the key digital image watermarking algorithms and techniques is

presented subsequently. The characteristics of watermarking systems are described for

evaluating the performance of watermarking systems. There are five important issues

that are usually considered in the most practical application; they are highlighted in the

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following subsections. In addition, digital watermarking is described as an efficient

method for the protection of ownership rights of digital audio, image, video and other

data types. It can be applied to different applications including digital signatures,

fingerprinting, broadcast and publication monitoring, authentication, copy control, and

secret communication. Watermarking attacks can be classified into two broad

categories: destruction attacks: including image compression, image cropping, spatial

filtering, etc., and synchronization attacks: including image rotation, image shifting and

pixelhine deletion. The chapter lists and describes some of these conventional attacks in

the following sections.

For constructing geometric invariant watermarking, four mainstream schemes

are introduced by literature reviews on watermarking algorithms robust to the

geometrical distortions. Most of these efforts confine to theoretically analyzing and

quantifying the effect of the global and local affine transform to the performance of the

watermarking algorithms.

2.2 HISTORY OF WATERMARKING

The thought to communicate secretly is as ancient as communication itself. First

stories appeared in the old Greek literature, which can be explained as early records of

covert communication, for example, in Homer‘s Iliad, or in tales by Herodotus. The

word ―steganography,‖ which is still used until today, derives from the Greek language

and means covert communication. In (Kobayashi M., 1997; Petitcolas F.A.P., et

al.1999), they have investigated the historical facts of covert communication in detail,

including the broad use of methods for covert and secret communication before and

during the two World Wars, and steganographic methods for analog signals. Although

the historical background is very interesting, this part does not cover it here in detail.

Please refer to (Kobayashi M., 1997; Petitcolas F.A.P., et al.1999) for an in-depth

inquisition of historic aspects.

Paper watermarks arose in the art of handmade papermaking about 700 years

ago. The oldest watermarked paper discovered in archives dates back to 1292 and has

its origin in Fabriano, Italy, which is considered the birthplace of watermarks. At the