{"http:\/\/dx.doi.org\/10.14288\/1.0065184":{"http:\/\/vivoweb.org\/ontology\/core#departmentOrSchool":[{"value":"Applied Science, Faculty of","type":"literal","lang":"en"},{"value":"Electrical and Computer Engineering, Department of","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/dataProvider":[{"value":"DSpace","type":"literal","lang":"en"}],"https:\/\/open.library.ubc.ca\/terms#degreeCampus":[{"value":"UBCV","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/creator":[{"value":"Paquet, Alexandre","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/issued":[{"value":"2009-09-29T23:19:19Z","type":"literal","lang":"en"},{"value":"2002","type":"literal","lang":"en"}],"http:\/\/vivoweb.org\/ontology\/core#relatedDegree":[{"value":"Master of Applied Science - MASc","type":"literal","lang":"en"}],"https:\/\/open.library.ubc.ca\/terms#degreeGrantor":[{"value":"University of British Columbia","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/description":[{"value":"The rapid expansion of the Internet and the overall development of digital\r\ntechnologies in the past years have sharply increased the availability of digital media.\r\nDigital contents can be reproduced without loss of quality, but they may also be\r\neasily modified, and sometimes, imperceptibly. In many contexts, any alteration of\r\nimage, video or audio data must be detected. Therefore, some work needs to be done\r\nto develop security systems to protect the content of digital data. Watermarking is\r\naccepted as a plausible candidate for such an application as it allows for the invisible\r\ninsertion of information in a host by its imperceptible modification.\r\nThis thesis is concerned with the protection of information contained in digital\r\nimages. A novel, semi-fragile watermarking technique for the authentication of\r\nimages is developed. Image protection is achieved by the insertion of a secret author's\r\nidentification key in an image's wavelet packet (WP) decomposition. Rounding\r\nthe mean of selected regions of WP coefficients embeds the binary key. To\r\ntake maximum advantage of the host image's characteristics in the embedding process,\r\nan optimal quantization protocol is formulated. The image's verification is\r\ndone without the use of the original unmarked image. The detection of unauthorized\r\nfrequency or spatial tampering with the image is performed by a combined\r\ninterband\/intraband verification protocol. This new technique can detect malicious\r\ntampering with images, but stays unaffected by high quality JPEG compression.","type":"literal","lang":"en"}],"http:\/\/www.europeana.eu\/schemas\/edm\/aggregatedCHO":[{"value":"https:\/\/circle.library.ubc.ca\/rest\/handle\/2429\/13336?expand=metadata","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/terms\/extent":[{"value":"6325205 bytes","type":"literal","lang":"en"}],"http:\/\/purl.org\/dc\/elements\/1.1\/format":[{"value":"application\/pdf","type":"literal","lang":"en"}],"http:\/\/www.w3.org\/2009\/08\/skos-reference\/skos.html#note":[{"value":"Wavelet Packets-based Digital Watermarking for Image Authentication by Alexandre Paquet B.Eng, Universite Laval, 2000 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF Master of Applied Science in THE FACULTY OF GRADUATE STUDIES (Department of Electrical & Computer Engineering) We accept this thesis as conforming to the required standard The University of British Columbia July 2002 \u00a9 Alexandre Paquet, 2002 In p resen t ing this thesis in partial fulfi lment of the requ i rements for an a d v a n c e d deg ree at the Univers i ty o f Brit ish C o l u m b i a , I agree that the Library shall make it freely available for reference a n d s tudy. I further agree that p e r m i s s i o n for ex tens ive c o p y i n g o f this thesis for scholar ly p u r p o s e s m a y b e g ran ted b y the h e a d of my depa r tmen t o r by his o r her representat ives . It is u n d e r s t o o d that c o p y i n g o r p u b l i c a t i o n of this thesis for financial gain shall no t be a l l o w e d w i t h o u t my wr i t t en p e r m i s s i o n . D e p a r t m e n t of ZJ^j^rJ 8 (cM\u00abf feny W The Univers i ty o f Brit ish C o l u m b i a V a n c o u v e r , C a n a d a D a t e A ^ , ^ f _ ? n r o D E - 6 (2\/88) Abstract The rapid expansion of the Internet and the overall development of digital technologies in the past years have sharply increased the availability of digital media. Digital contents can be reproduced without loss of quality, but they may also be easily modified, and sometimes, imperceptibly. In many contexts, any alteration of image, video or audio data must be detected. Therefore, some work needs to be done to develop security systems to protect the content of digital data. Watermarking is accepted as a plausible candidate for such an application as it allows for the invisible insertion of information in a host by its imperceptible modification. This thesis is concerned with the protection of information contained in dig-ital images. A novel, semi-fragile watermarking technique for the authentication of images is developed. Image protection is achieved by the insertion of a secret au-thor's identification key in an image's wavelet packet (WP) decomposition. Round-ing the mean of selected regions of WP coefficients embeds the binary key. To take maximum advantage of the host image's characteristics in the embedding pro-cess, an optimal quantization protocol is formulated. The image's verification is done without the use of the original unmarked image. The detection of unautho-rized frequency or spatial tampering with the image is performed by a combined interband\/intraband verification protocol. This new technique can detect malicious tampering with images, but stays unaffected by high quality JPEG compression. ii Contents Abstract ii Contents iii List of Tables vii List of Figures viii List of Abbreviations xiii Glossary xiv Acknowledgements xv 1 Introduction 1 1.1 Digital Watermarking 1 1.2 Problem Definition 3 1.3 Organization of the Thesis 4 2 Wavelet Analysis 7 iii 2.1 Introduction 7 2.2 Historical Perspective 8 2.3 Wavelets and Multiresolution Analysis 14 2.3.1 Series Expansion 14 2.3.2 Filter Banks 16 2.3.3 Multiresolution and Wavelet Theory 23 2.4 Wavelet Packet Analysis 28 2.5 Multidimensional Signals 32 2.6 Summary 33 3 Digital Watermarking 34 3.1 Introduction 34 3.1.1 Definition of Watermarking 35 3.2 Historical Perspective 36 3.3 Background on Watermarking 38 3.3.1 Host Media for Watermarking 40 3.3.2 Applications of Watermarking 41 3.3.3 Requirements of Watermarking Systems 42 3.3.4 Embedding Domains and Decoding Procedures . . . . 43 3.4 Watermarking for Copyright Protection 45 3.5 Summary 50 4 Image Authentication 52 4.1 Introduction 52 4.2 Approaches to authentication 54 iv 4.3 Requirements of Authentication Schemes 56 4.4 Previous Work 58 4.4.1 Fragile Watermarking in the Spatial Domain 58 4.4.2 Fragile Watermarking in Transform Domains 61 4.5 Our WP-Based Image Authentication 69 4.5.1 Embedding Process 70 4.5.2 Optimal Quantization Step 81 4.5.3 Watermark Decoding Process 84 4.6 Summary 89 5 Experimental Results 91 5.1 Introduction 91 5.2 Embedding, Decoding and Visibility 95 5.3 Tampering Detection 107 5.4 _ Comparison with Ei'konamark 112 5.4.1 Image Quality and Tampering Detection 113 5.4.2 Resistance to Collage Attacks . 119 5.4.3 Summary of Comparisons 122 5.5 Robustness to J P E G Compression 123 5.5.1 Predistortion in the Spatial and Wavelet Domains . . . 125 5.6 Summary 130 6 Conclusions and Future Research 132 6.1 Overview 132 v 6.2 Digital Watermarking and Content Authentication 133 6.2.1 Our Wavelet Packets-Based Authentication Scheme 135 6.2.2 Review of Results 136 6.3 Future Research 138 6.4 Closing Remarks 140 Bibliography 141 Appendix A 150 A . l Fourier Analysis 150 A.2 Orthonormality of Haar Basis 151 A.3 Conditions of Filters Ht(z) and Fi(z) 152 A.4 Definition of Multiresolution 153 A.5 Steps towards Multiresolution 154 A.6 Erasable Watermarking 154 Appendix B 156 vi List of Tables 4.1 Optimum Step Sizes for Laplacian Distribution with a2 = 1 (from [65]) 84 5.1 Average PSNR for Different Wavelet Functions 103 5.2 Average Detection Rate for Different Wavelet Functions . . . . 107 vii List of Figures 2.1 Haar Scaling (<\/>) and Wavelet (ip) Functions 9 2.2 Meyer Scaling (
) and Wavelet (ip) Functions 9 sines and cosines, which are both infinite in time, the standard Fourier de-composition lacks the time1 localization necessary for the accurate analysis of several real signals. The idea of WFA is to study the frequencies of a signal for time-limited windows. This allows for some time localization of the frequency characteristics of a given signal. WFA concept finally allowed the examination of things in terms of both time and frequency. Nonetheless, Haar remained the only example of a wavelet, and the next major advancements did not come until later in the 1980s. Jean Morlet and Alex Grossman teamed up in 1981. Together, they discovered that a signal could be transformed into wavelet form, and then synthesized back into the original signal without any loss of information. Then, in 1984, they were the first to use the term wavelet to describe their functions [30]. More specifically, they were called Wavelets of Constant Slope. Other researchers had used the term wavelets for different signal processing applications (see [61] for example) but Morlet and Grossman were the first to use it as it is now currently referred to, which is as follows: a wavelet is a unique function, limited in time and frequency, that can be translated and dilated to form multiresolution basis used to decompose a signal at different levels. In addition, their major contribution was the finding of a simple signal recomposition method from its wavelet coefficients. They also discovered an-1Time and space will be used alternatively throughout this thesis as time is, in fact, but only one possible space representation. However, since a lot of concepts used have first been developed for time-dependant signals, we find it helpful to use the same notation. 10 other interesting thing that is now commonly used in wavelet-based coding: a small modification in the wavelet coefficients only causes a small change in the original signal. This might not have appeared to be especially meaning-ful at the time, but when considering that modern wavelet-based compression schemes quantize wavelet coefficients, if it had been otherwise, data compres-sion would be a much more difficult task today. The real breakthrough in wavelets analysis, however, happened in the late 1980's when a lot of papers now considered classic were published. Yves Meyer and Stephane Mallat were two important contributors to this newborn field. Investigating the use of wavelets in many different applied fields, they were amongst the first to develop the concept of multiresolution analysis for wavelets [49]. This was an important step for the advancement of research on wavelets. As a result, multiresolution is now an extensively used signal decomposition approach. Mallat and Meyer were the first to mention scaling functions of wavelets, which allow researchers and mathematicians to construct their own wavelets using established criteria [80]. Around the same time, a Belgian physicist named Ingrid Daubechies employed multiresolution analysis to create her own family of wavelets. Using construction methods related to filter banks, she introduced in [23] a family of compactly supported orthogonal wavelet systems with arbitrarily high, but fixed regularity. These wavelets offer a number of desirable properties (such as compact support, orthogonality, regularity, and continuity) that make them trully attractive2. This is why the Daubechies Wavelets are now some of the 2 More on this in the next section. 11 -5 0 5 -5 0 5 Figure 2.2: Meyer Scaling (<\/>) and Wavelet (ip) Functions 12 most common ones today. ft Db4: \\|\/ i i 1 \"\"0 2 4 6 0 2 4 6 Figure 2.3: Daubechies-4 Scaling (