||Development of Watermarking Scheme for Halftone Images
||Department of Electrical Engineering
Particle swarm optimization
Support vector machine
Human visual system
||As audio, video, images and other works become available in digital form, the ease with which perfect copies can be made, may lead to large-scale unauthorized copying which might undermine the music, picture, book, and software publishing industries. Watermarking has been considered for many copy prevention and copyright protection applications. Watermarking is the practice of hiding a message about an image, audio chip or meaningful logo within that work itself for protecting copyright. Two types of watermarking can be identified depends on specific application-driven requirements: (a) robust watermarking, and (b) fragile watermarking. For the aforementioned schemes, each watermarking scheme has its own considerations and limitations. In this dissertation, we are trying to develop robust and fragile watermarking schemes respectively and explore the combined watermarking schemes for the halftone images. Halftone images are widely used in the printing of books, magazines, newspapers and computer printers. Although there are only 2 tones, halftone images look like the original multi-tone images when viewed from a distance.
The first issue of this dissertation is to introduce the robustness and fragility of the watermarking system for digital gray-level image. The robust watermarking scheme is used for copyright protection and fragile watermarking scheme is used for content authentication. The second issue of this dissertation focuses on embedding the watermarks in the halftone images. The optimized watermarking scheme for halftone images based on dithering method is proposed. The multi-purpose watermarking is proposed for not only protecting copyright but also authenticating content. The optimized dither cells for watermarks embedding would be generated by the particle swarm optimization (PSO) with high transparency. However, in the experiments, we find that the proposed multi-purpose watermarking is not robust to the scaling attacks.
In the third issue of the dissertation, we would try to find the more robust watermarking scheme and discuss the image quality criterion for halftone images. In the method, the frequency domain based watermarks would be embedded to the watermarked images in order to enhance the robustness for scaling attack and call it hybrid watermarking scheme. And the human visual filter for the image quality criterion in halftone images is generated by PSO.
The last issue of this dissertation is to explore the advanced method for the proposed robust watermarking system for halftone images. The section aims at enhancing the privacy of the watermarking scheme that only one dither cell is necessary in the watermark extracting system. The support vector machine is adopted as the classification function in the watermark extracting stage, and the automatic resynchronization procedure for the watermark extraction stage with print-and-scan attacks is proposed to resist scan-and-print attacks.
||CH 1 Introduction 1
1.1 Introduction of Watermarking 1
1.2 Watermarking Schemes for Halftone Images 4
1.3 The Organization of This Dissertation 6
CH 2 Related Watermarking Works 7
2.1 Related Robust Watermarking Works 7
2.2 Related Fragile Watermarking Works 8
CH 3 Watermarking Scheme in Halftoning Process 10
3.1 Introduction 11
3.2 Halftoning 14
3.3 Proposed watermarking schemes 17
3.3.1 Watermark Embedding 17
3.3.2 Watermark Extracting 19
22.214.171.124 Robust Watermark Extracting 19
126.96.36.199 Fragile Watermark Extracting 21
3.4 Dither Cell Generation 23
3.4.1 Particle Swarm Optimization 23
3.4.2 Dither Cell Generation Using Particle Swarm Optimization 25
3.5 Geometric Invariance 27
3.5.1 Zernike transform 28
3.5.2 Recovering 31
3.6 Simulations 33
3.6.1 Image Quality 33
3.6.2 Copyright Protection 34
3.6.3 Content Authentication 37
3.6.4 Comparison 39
3.6.5 Resistance geometric attacks 40
3.7 Summary 43
CH 4 Hybrid Watermarking Scheme For Halftone Images 44
4.1 Introduction 45
4.2 Binary Pseudo-wavelets Transform 47
4.3 Proposed Watermarking System 48
4.3.1 Watermark Embedding 49
4.3.2 Watermark Extracting 51
4.4 Quality Criterion 53
4.5 Robustness Enhancement 57
4.6 Simulations 58
4.6.1 Image Quality 58
4.6.2 Copyright Protection 59
4.6.3 Comparison 62
4.6.4 Discussion 63
4.7 Summary 66
CH 5 Improved Watermarking Scheme For Halftone Images . 67
5.1 Introduction ........................................................................................................... 68
5.2 Support Vector Machine ........................................................................................ 70
5.3 Proposed Watermarking Scheme........................................................................... 73
5.3.1 Watermark Embedding ............................................................................... 74
5.3.2 Watermark Extracting ................................................................................. 75
188.8.131.52 SVM Training .................................................................................. 77
184.108.40.206 Owner Watermarks Extracting ........................................................ 77
5.3.3 Watermark Extraction with Print-and-Scan Attack .................................... 78
5.4 Simulations ............................................................................................................ 81
5.4.1 Watermarked Image Quality and Its Capacity ............................................ 82
5.5 Summary ................................................................................................................ 87
CH 6 Conclusions And Future Works ......................................... 88
References ...................................................................................... 90
List of Figures
Figure 3.1 The error diffusion algorithm. 14
Figure 3.2 Error filters of different error diffusion methods. 15
Figure 3.3 The proposed robust and fragile watermark embedding systems. 18
Figure 3.4 The proposed robust and fragile watermark extracting systems. 18
Figure 3.5 Sub-images (co-centric ring) of Lena image with difference width, w. 30
Figure 3.6 (a) Original image (b) Halftone image (c) Robust watermark (d) Fragile watermark. 33
Figure 3.7 Some attacks (a) Cropping (b) Drawing in black (c) Drawing in black and white (d) Rotating. 35
Figure 3.8 Extracted robust watermarks from images under attack (a) to (p). 35
Figure 3.9 DR of the extracted robust watermark under the attack (a) to (p). 35
Figure 3.10 DR of robust watermark detector to 1000 random Keys. (The 500th key is the key S we actually obtained from embedded system.) 36
Figure 3.11 DR of fragile watermark detector to 1000 random Keys. (The 500th key is the key, S_D, we actually obtained from embedded system.) 37
Figure 3.12 Extracted fragile watermark under the attack (a) to (p). 38
Figure 3.13 DR of the extracted fragile watermark under the attack (a) to (p). 38
Figure 3.14 MPSNR value simulated by various sizes of dither cell. 39
Figure 3.15 Examples of rotated images with other malicious attacks. (rotating angle = 30) 40
Figure 3.16 Rotated Lena image with 10% and 20% cropping. 42
Figure 4.1 BPWT coefficients’ position labeling. 48
Figure 4.2 Proposed watermark embedding system. 49
Figure 4.3 Proposed watermarks extracting system. 49
Figure 4.4 PSO trained human visual filter of size 7×7. 56
Figure 4.5 Extracted spatial domain based watermarks under malicious attacks. 60
Figure 4.6 Extracted frequency domain based watermarks under malicious attacks. 61
Figure 4.7 DR of extracted spatial and frequency domain based watermarks under malicious attacks. 63
Figure 4.8 Correlation between the block halftoning image, H_(C_0)^n and H_(C_1)^n, without frequency domain based watermarks. 65
Figure 4.9 Correlation between the block halftoning image, H_(C_0)^n and H_(C_1)^n, with frequency domain based watermarks. 65
Figure 5.1 The basic theory of SVM. 70
Figure 5.2 Structure of the improved watermarks embedding system. 73
Figure 5.3 Structure of robust watermarks extracting system. 74
Figure 5.4 The structure of print-and-scan model. 78
Figure 5.5 The structure of the procedure against print-and-scan attacks model. 79
Figure 5.6 (a-d) Cropped watermarked Lena image with 100x100, 150x150, 200x200, and 250x250 pixels respectively. 81
Figure 5.7 (a-d) Boundary Cropped watermarked Lena image with 5x5, 10x10, 20x20, and 30x30 respectively. 83
Figure 5.8 Tampering attack. 83
Figure 5.9 (a-c) The watermarked image under 10%, 20%, 30% noise (salt and pepper) attacks respectively. 85
List of Tables
Table 3.1 Dither cell (Bayer-5). 16
Table 3.2 Visual quality measured by MPSNR(dB) 38
Table 3.3 Capacity tested by various sizes of dither cell. 39
Table 3.4 Estimation errors of the multi-rings Zernike Transform tested by different widths. 40
Table 3.5 Detecting angles estimated by the Zernike moment method and the proposed method. 40
Table 3.6 Angle estimation of the rotated image in 30 degree under cropping attacks. 42
Table 3.7 Scaling factor estimation of the scaled image under cropping attacks. 42
Table 4.1 Binary pseudowavelet basis and its inverse basis. 48
Table 4.2 Visual quality simulated by different human visual filter’s size. 55
Table 4.3 Image quality of watermarked halftone image 58
Table 4.4 Visual quality simulated by different human visual filter. 58
Table 4.5 DR of spatial and frequency domain based watermarks under malicious attacks. 62
Table 4.6 Visual quality comparisons measured by PPSNR. 63
Table 4.7 Correlation between the halftoning sub-image and reference sub-image with or without frequency domain based watermarks. 66
Table 5.1 Visual quality simulated by different human visual filter. 81
Table 5.2 DR of the extracted watermark with cropping attacks simulated by various watermarking schemes. 81
Table 5.3 DR of the extracted watermark attacked by boundary cropping. 83
Table 5.4 DR of the extracted watermark under tampering attack. 84
Table 5.5 DR of the extracted watermark under noise (salt and pepper) attacks. 84
Table 5.6 DR of the extracted watermark with noise attack tested by various reference watermark lengths. 84
Table 5.7 DR of the extracted watermark under print-and-scan attacks. 84
Table 5.8 DR of the extracted watermark under scaling attack. 85
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