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研究生: 裴哈利
Heri - Prasetyo
論文名稱: 可靠的SVD-Scheme 圖像浮水印
The Reliable SVD Scheme Image Watermarking
指導教授: 洪西進
Shi-Jinn Horng
口試委員: 陳秋華
Chyou-hwa Chen
王毓饒
Yuh-Rau Wang
學位類別: 碩士
Master
系所名稱: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
論文出版年: 2009
畢業學年度: 97
語文別: 英文
論文頁數: 59
中文關鍵詞: 浮水印SVD
外文關鍵詞: principal component, false positive problem, ambiguous situation
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可靠的SVD浮水印主要是用來解決SVD浮水印的缺點。就本論文所提出的方法,主要是使用離散餘弦變換(DCT)和離散小波變換(DWT),把浮水印嵌入到原本的影像中,這方法可以有效的使得SVD subspace 保留圖片重要的資訊。粒子群優(PSO)是用來找出合適的排例因子來得到建全的浮水印。實驗結果經由結合已存在的方法能有效的增進了效能。


The reliable SVD based watermarking is proposed to overcome the major drawbacks of SVD based image watermarking. In the proposed method, the principal components of the watermark are embedded into the host image in discrete cosine transform (DCT) and discrete wavelets transform (DWT). It makes use of the fact that the SVD subspace can preserve significant information of an image. The particle swarm optimization (PSO) is used for finding the suitable scaling factors to achieve the robust watermarking. The experimental result demonstrates the improved performance of the proposed method in comparison with the existing method.

Chapter 1 Introduction - 1 - 1.1 Motivation - 1 - 1.2 Research Focus and Contribution - 3 - 1.3 Structure of the Work - 4 - Chapter 2 SVD-based Image Watermarking and Its Drawbacks - 5 - 2.1 Singular Value Decomposition (SVD) - 5 - 2.2 SVD Based Image Watermarking - 10 - 2.3 Drawback In SVD Based Image Watermarking - 12 - Chapter 3 Reliable SVD-based Watermarking - 17 - 3.1 A Reliable SVD based Watermarking Scheme - 17 - 3.2 Problem with the Reliable SVD Based Watermarking Scheme - 21 - Chapter 4 Proposed Method - 25 - 4.1 Basic Idea - 25 - 4.2 The Reliable SVD Based Image Watermarking Scheme - 26 - 4.2.1 First Method : DCT-SVD Based Image Watermarking - 26 - 4.2.2 Second Method : DWT-SVD Based Image Watermarking - 28 - 4.3 Computing Scaling Factor Using Particle Swarm Optimization - 30 - Chapter 5 Experimental Result - 34 - 5.1 Host image and watermark image - 34 - 5.2 The PSO parameters - 35 - 5.3 Result of PSO Algorithm - 36 - 5.4 Robustness Test For The First Method - 37 - 5.5 Robustness test for the second method - 42 - 5.6 Comparison - 46 - Chapter 6 Conclusion - 48 - 6.1 Conclusion - 48 - 6.2 Future Work - 48 - Reference - 50 -

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[19] Particle Swarm Optimization, http://www.swarmintelligence.org/.

[20] Particle Swarm Optimization, http://www.engr.iupui.edu/~shi/Coference/
psopap4.html

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svd.pdf

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