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系統識別號 U0002-1301200913143900
中文論文名稱 半色調影像浮水印系統之研究
英文論文名稱 Development of Watermarking Scheme for Halftone Images
校院名稱 淡江大學
系所名稱(中) 電機工程學系博士班
系所名稱(英) Department of Electrical Engineering
學年度 97
學期 1
出版年 98
研究生中文姓名 吳業寬
研究生英文姓名 Yeh-Kuang Wu
學號 891350034
學位類別 博士
語文別 英文
口試日期 2009-01-05
論文頁數 99頁
口試委員 指導教授-謝景棠
委員-陳稔
委員-施國琛
委員-顏淑惠
委員-黃仁俊
委員-謝景棠
中文關鍵字 浮水印  半色調  粒子群最佳化  支援向量機  人眼視覺系統 
英文關鍵字 Watermark  Halftone image  Particle swarm optimization  Support vector machine  Human visual system 
學科別分類
中文摘要 數位多媒體資訊被廣泛地運用於我們的日常生活當中,人們可以輕易地在未經擁有者同意的情況下,恣意地複製與傳播。數位浮水印技術正是一個解決此問題的好方法,實行數位多媒體版權的認證與智慧財產權的保護。數位浮水印技術中,根據嵌入浮水印的目的不同,可分為強健型與易碎型浮水印系統,每種系統都有其一定的限制與考量;本論文中,我們將分別針對此兩系統提出新的策略,應用於數位影像中,同時並將其擴展於數位半色調影像中。相較於灰階影像,數位半色調影像僅需使用極少的記憶體儲存空間,不僅利於網路上的傳輸,亦能產生近似灰階的效果,遂其廣泛地利用在網路與列印之相關應用中。
本論文中,我們將先簡介強健型與易碎型浮水印系統之特性。強健型浮水印可運用於智慧財產權之保護,而易碎型浮水印可運用於資料之保護與認證。
本論文的第二個主軸將探討應用於半色調影像中之浮水印系統。首先,我們提出了一個多目標之半色調影像浮水印系統,多目標即代表此浮水印系統同時擁有強健型浮水印及易碎型浮水印之特性。藏有浮水印之半色調影像必須能同時具有透視性與強健性,在本系統中,我們引用粒子群最佳化演算法,藉以找到最佳化之點陣式矩陣,並經由數個矩陣之選擇,嵌入強健型浮水印系統。同時為了不影響強健型浮水印之強健性,我們將易碎型浮水印嵌入於強健型浮水印之鎖鑰中。
經由模擬,我們發現上述之方法,並不能抵抗影像尺寸縮小放大之攻擊;有鑒於此,我們提出了第二種半色調影像浮水印系統,稱之為混合型浮水印系統,此系統結合了上述之多目標系統,並於嵌入了另一個浮水印於頻率域上,藉以增加浮水印系統之強健度。同時為了衡量半色調影像浮水印之品質,我們利用粒子群最佳化演算法,提出了人眼視覺濾波器,應用於PSNR-影像衡量法中,經由人眼視覺濾波器可以將半色調影像較為準確地近似至灰階影像。
然而,上述之方法,在其浮水印抽取步驟中,皆須使用此多個半色調矩陣。最後,我們試著提出一進階型浮水印系統,此系統將簡化浮水印抽取時所需要的事前資訊,以確保浮水印系統之隱密性,並避免竄改者恣意產生未經許可,確具有浮水印之半色調影像。同時,對於列印與掃描攻擊,我們提出一套自動化同步系統應用於浮水印抽取系統中,包含所有相關之前處理作業。
英文摘要 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.
論文目次 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
3.3.2.1 Robust Watermark Extracting 19
3.3.2.2 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
5.3.2.1 SVM Training .................................................................................. 77
5.3.2.2 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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