Robust digital image watermarking algorithms for copyright protection [Elektronische Ressource] / von Nataša Terzija
173 pages
English
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Robust digital image watermarking algorithms for copyright protection [Elektronische Ressource] / von Nataša Terzija

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173 pages
English

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Robust digital image watermarking algorithms for copyright protection Von der Fakultät für Ingenieurwissenschaften der Universität Duisburg-Essen zur Erlangung des akademischen Grades einer Doktorin der Ingenieurwissenschaften genehmigte Disertation von Nataša Terzija aus Belgrad Referent: Prof. Dr. Walter Geisselhardt Korreferent: Prof. Dr. Josef Pauli Tag der mündlichen Prüfung: 18.10.2006 Acknowledgement: I would like to express my thanks to Prof. Walter Geisselhardt, who supervised my academic activities at the University Duisburg-Essen and gave me freedom and optimal conditions necessary for my research. I especially thank Prof. Josef Pauli, who read the manuscript and provided corrections and useful comments. Thanks are also due to Prof. Hans-Dieter Kochs, who has also supported my research, and to all those outstanding individuals with whom I have worked in the past, who helped me understanding watermarking and its applications better, including Prof. Gerlind Plonka-Hoch and Prof. Zoran Bojkovic (Univ. of Belgrade, Serbia). I would also like to thank my whole family for encouragement and love. Without it, my thesis could never have been completed. Keywords Digital image watermarking, Scale invariant feature point detectors, Image registration, Synchronization technique for watermark detection, Discrete Wavelet Transform, Complex Wavelet Transform, Error Correction Codes.

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Publié par
Publié le 01 janvier 2006
Nombre de lectures 30
Langue English
Poids de l'ouvrage 4 Mo

Exrait




Robust digital image watermarking
algorithms for copyright protection


Von der Fakultät für Ingenieurwissenschaften
der Universität Duisburg-Essen
zur Erlangung des akademischen Grades einer
Doktorin der Ingenieurwissenschaften


genehmigte Disertation
von
Nataša Terzija
aus Belgrad



Referent: Prof. Dr. Walter Geisselhardt
Korreferent: Prof. Dr. Josef Pauli
Tag der mündlichen Prüfung: 18.10.2006
Acknowledgement:



I would like to express my thanks to Prof. Walter Geisselhardt, who supervised my
academic activities at the University Duisburg-Essen and gave me freedom and optimal
conditions necessary for my research. I especially thank Prof. Josef Pauli, who read the
manuscript and provided corrections and useful comments.
Thanks are also due to Prof. Hans-Dieter Kochs, who has also supported my
research, and to all those outstanding individuals with whom I have worked in the past, who
helped me understanding watermarking and its applications better, including Prof. Gerlind
Plonka-Hoch and Prof. Zoran Bojkovic (Univ. of Belgrade, Serbia).
I would also like to thank my whole family for encouragement and love. Without it,
my thesis could never have been completed.

Keywords



Digital image watermarking, Scale invariant feature point detectors, Image registration,
Synchronization technique for watermark detection, Discrete Wavelet Transform, Complex
Wavelet Transform, Error Correction Codes.




ABSTRACT

Digital watermarking has been proposed as a solution to the problem of resolving
copyright ownership of multimedia data (image, audio, video). The work presented in this
thesis is concerned with the design of robust digital image watermarking algorithms for
copyright protection.
Firstly, an overview of the watermarking system, applications of watermarks as well
as the survey of current watermarking algorithms and attacks, are given. Further, the
implementation of feature point detectors in the field of watermarking is introduced. A new
class of scale invariant feature point detectors is investigated and it is shown that they have
excellent performances required for watermarking.
The robustness of the watermark on geometrical distortions is very important issue
in watermarking. In order to detect the parameters of undergone affine transformation, we
propose an image registration technique which is based on use of the scale invariant feature
point detector. Another proposed technique for watermark synchronization is also based on
use of scale invariant feature point detector. This technique does not use the original image
to determine the parameters of affine transformation which include rotation and scaling. It is
experimentally confirmed that this technique gives excellent results under tested
geometrical distortions.
In the thesis, two different watermarking algorithms are proposed in the wavelet
domain. The first algorithm belongs to the class of additive watermarking algorithms which
requires the presence of original image for watermark detection. Using this algorithm the
influence of different error correction codes on the watermark robustness is investigated.
The second algorithm does not require the original image for watermark detection. The
robustness of this algorithm is tested on various filtering and compression attacks. This
algorithm is successfully combined with the aforementioned synchronization technique in
order to achieve the robustness on geometrical attacks.
The latter watermarking algorithm presented in the thesis is developed in complex
wavelet domain. The complex wavelet transform is described and its advantages over the
conventional discrete wavelet transform are highlighted. The robustness of the proposed
algorithm was tested on different class of attacks. Finally, in the thesis the conclusion is
given and the main future research directions are suggested.
Content:



1. Introduction 1
1.1 Importance of Digital Watermarking and Watermarking Applications 1
1.2 Motivation 3
1.3 Thesis contribution 5
1.4 Thesis organization 6

2. Introduction to Watermarking Technology 8
2.1 Types of digital watermarks 8
2.2 Watermark requirements for still images 11
2.3 Structure of a typical watermarking system 12
2.3.1 Embedding process 12
2.3.2 Extraction/detection process 13
2.4 Watermarking as a communication problem 14
2.5 Properties of the watermark 17
2.6 Watermarking Algorithms 25
2.7 Benchmarking and performance evaluation of watermarking schemes 26
2.8 Watermarking for copyright protection 27
2.9 Chapter Sumary 30

3. Watermarking Techniques Based on the use of Feature Point
Detcors 31
3.1 Introduction 31
3.2 Difference of Gaussian feature point detector 35
3.3 Comparison of the feature point detectors 41
3.4 Chapter Sumary 49

i4. The synchronization issue in watermarking schemes 50
4.1 Image registration techniques 51
4.2 Watermarking techniques dealing with the problem of desynchronisation
without access to the original image content 57
4.2.1 Exhaustive search 57
4.2.2 Periodical sequences
4.2.3 Invariant domains 58
4.2.4 Synchronization marks (pilot signals, template) 59
4.2.5 Content based approaches 63
4.3 Proposed synchronization technique 63
4.3.1 Fourie transform 64
4.3.2 Description of the proposed synchronization technique 69
4.3.2.1 Template embedding algorithm 72
Template extraction algorithm 73
4.3.3 Discussion about the relevant parameters of the proposed technique 76
4.3.4 Testing result 82
4.3.5 Comparison with other techniques and the advantages of the proposed
technique 3
4. Chapter Sumary 84

5. Digital image watermarking in wavelet domain 85
5.1 Discrete wavelet transform 85
5.2 Properties of thewavelet transform 89
5.3 HVS Perceptual models based on DWT 90
5.4 Algorithms Classification 91
5.5 The non-blind additive watermarking algorithm (NB-T01) 92
5.5.1 The watermark embedding procedure 92
5.5.2 Watermark extraction procedure 94
5.5.3 Algorithm NB-T01 testing 94
5.5.4 Impact of different Reed-Solomon codes 98
5.5.5 Improvement of the algorithm 98
5.6 Proposed blind watermarking algorithm (B-T02) 98
ii5.6.1 Embedding procedure 99
5.6.2 Detection procedure 101
5.6.3 Algorithm B-T02 Testing 103
5.6.4 Robustness on geometrical attacks 112
5.7 Chapter Summary 113

6. Digital image watermarking in complex wavelet domain 114
6.1 Introduction 114
6.2 Complex wavelet transform and its properties 115
6.3 Watermarking algorithms 119
6.4 The new watermarking algorithm based on DT - CWT (C - T03) 121
6.4.1 Embedding procedure 121
6.4.2 Detection procedure 125
6.4.3 Testing results (C-T03) 126
6.4.4 Robustness on geometrical attacks 130
6.5 Chapter Summary 132

7. Conclusion and Future Research Directions 133
7.1 Thesis Review 133
7.2 Future Research Directions 136

Apendices 138
Appendix A Comparison of Feature Points Detectors: Results 139
Appendix B Radon Transform 142
Appendix C Fourier-Mellin Transform 143
Appendix D Shift Invariance by Parallel Filter Banks 144
Appendix E Test Images 148

List of Publications 149
References 150

iiiACRONYMS



ACF autocorrelation function
AWGN additive Gaussian white noise
bpp bit per pixel
BCH Bose-Chaudhuri-Hocquenghem error correction code
CWT Complex Wavelet Transform
DCT Discrete Cosine Transform
DFT Discrete Fourier Transform
DT-CWT Dual-Tree Complex Wavelet Transform
DWT Discrete Wavelet Transform
ECC Error Correction Code
FMT Fourier-Mellin Transform
HVS Human Visual System
ICWT Inverse CWT
IDWT Inverse DWT
JND just noticeable difference
JPEG Joint Photographic Expert Group
LoG Laplacian of Gaussian
LPM Log Polar mapping
LSB Least Significant Bit
MSE Mean Square Error
PR Perfect Reconstruction
PSNR Peak Signal to Noise Ratio
RS Read-Solomon error correction code
QIM Quantisation Index Modulation
SIFT Scale Invariant Feature Transform
SS Spread Spectrum
iv
List of Figures



1.1 Thesis organization. 7

2.1 Types of watermarking techniques. 9
2.2 Embedding unit, as a part of a watermarking system. 13
2.3 The extraction/detection process. 14
2.4 Standard model of communication system. 15
2.5 Random geometric distortion model. 23

3.1 Feature points of the Lena image extracted with the Harris corner detector. 33
3.2 Feature point on Lena image detected with Harris-Affine detector. 34
3.3 Feature point on Lena image detected with SIFT detector. 34
3.4 The pyramid of difference of Gaussian. 38
3.5 Detecting of maximum and minimum of SIFT images. 38
3.6 Computation of keypoint descriptors. 40
3.7 Feature points extracted on Lena image with SIFT and Harris-Affine detector. 45
3.8 The average number of the corresponding points after non-geometrical
distortions. 47
3.9 The average number of the corresponding after rotation and image scaling
distortions. 48
3.10 The average number of the corresponding after image cropping and
combination of rotation and image scaling distortions. 48

4.1 (a) Original Lena image. (b) Lena image after G1 attack. The corresponding
feature points are presented on the both images. (c) Reconstructed image. 54
4.2 (a) Original Lena image. (b) Lena image after G2 attack. The corresponding
feature points are presented on the both images. (c) Reconstructed image. 54
4.3 (a) Original Lena image. (b) Lena image after G3 attack. The corresponding
vfeature points are presented on the both images (c) Reconstructed image. 55
4.4 (a) Original Lena image. (b) Lena image after G4 attack. The corresponding
feature points are presented on the both images. (c) Reconstructed image. 55
4.5 (a) Original Lena image. (b) Lena image after G5 attack. The corresponding
feature points are presented on the both images (c) Reconstructed image. 55
4.6 (a) Original Lena image. (b) Lena image after G6 attack. The corresponding
feature points are presented on the both imagimage. 56
4.7 Rotation, scale, translation invariant Fourier Mellin domain. 59
4.8 The examples of different templates used in the DFT domain. 59
4.9 Radon Transform of: (a) Lena image; (b) rotated Lena image for 30. 61
4.10 Log-polar mapping, inverse log-polar mapping and difference image. 69
4.11 Embedding of two template point structures in DFT domain. 71
4.12 One example of embedded template points in log-polar coordinates of
Fourie spectra. 73
4..13 The circular regions selected for template embedding. 76
4.14 The peaks of cross-correlation function. 78
4.15 . Log-polar representation of the region with and without template points 79
4.16 Template peaks extracted after geometrical distortion and corresponding
cross correlation peaks. 80
4.17 Lena image with embedded template points; difference image and circle
regions around the feature points containing the template. 82

5.1 One level of decomposition of two-dimensional DWT. 87
5.2 The pyramidal structure. 88
5.3 Two-level DWT decomposition of Lena image obtained by using the Haar
wavelt filer. 88
5.4 Block diagram ofthe embedding method. 93
5.5 The testing results for different filtering attacks, JPEG2000 and JPEG
compression attacks. 97
5.6 The results of our watermarking algorithm for different attacks: 109
5.7 Watermarked Barbara image and difference image. 111

6.1 Analysis and synthesis filter banks for the dual-tree discrete CWT. 117
vi
D