臺灣博碩士論文加值系統

由於數位化時代的來臨以及網際網路使用上的普及，使得數位化型式的媒體可以很輕易地遭到竄改、複製和散佈，進而造成原著作者或是版權擁有者的權益受到損害。於是保護數位化資訊的智慧財產權也日漸受到重視，因為數位浮水印是個簡單而且有效的數位化媒體智慧財產權的保護方式，所以近年來已有許多的學者投入了相當多的研究在數位浮水印的領域之中。
在本論文中提出的強健型及盲目取出之浮水印方法，主要是應用於保護數位化靜態影像的智慧財產權，此方法基本上是利用小波轉換，分別針對位於不同頻帶小波係數特性上的差異，而分別使用(1)以通訊觀念為基礎的Lattice codes編碼方式；(2)以失真察覺臨界值(JND, Just Noticeable Distortion)為基礎的不重要係數修改法；(3)以奇異值分解(SVD, Singular Value Decomposition)之量化調變(QIM, Quantization Index Modulation)等三種方法，並且在浮水印嵌入之前使用Arnold轉換對浮水印進行置亂處理以增加浮水印的安全性。在能夠維持原來影像不被破壞的前提之下，而達到嵌入強健型浮水印的目的。從實驗結果可知，本論文所提出的浮水印方法，可以抵抗各式影像失真壓縮的攻擊、常用的影像處理攻擊以及幾何方式的攻擊。另外，由於本方法是盲目取出的浮水印方法，也就是在取出浮水印時不需要原始影像或是與其所相關的資訊，只需要嵌入參數即可取出浮水印，所以是個非常方便且實用性高的浮水印方法。

Because of the arrival of digital world, media in digital form can be easily altered, duplicated and spread which causes the rights and interests of original author and copyright owner are compromised or destroyed. Therefore, the protection of the intellectual property rights of digital information has come to attention. Digital watermark provides a simple and effective way of protection for property of the kind. Nowadays, more and more studies have been thrown to the field of digital watermark.

In this thesis, a method of robustness and blind extraction watermark has been put forward. The main purpose of this method is to protect the intellectual property right of static images. The basic concept is to utilize wavelet transformation where, according to different characteristic coefficient on different band, three methods will be applied respectively as following.
1. Lattice code based on communication principle
2. Insignificant coefficient based on JND of human visual model
3. Quantization Index Modulation based on SVD
The three methods above will achieve the goal of embedding watermark on the premise of keeping image fidelity.

The experimental results prove that the proposed method presented in this thesis will have immunity to the attacks of various image compression, commonly used image processing methods and geometric methods. Additionally, because this approach is blind watermark, which means neither original image and nor its related information is needed, it is a very convenient and practical method of watermark.

摘 要 ii
ABSTRACT iii
誌　謝 iv
目　錄 v
表目錄 viii
圖目錄 ix
第一章 緒 論 1
1.1 前言 1
1.2 研究動機 2
1.3 論文架構 3
第二章 數位浮水印的簡介 4
2.1 何謂數位浮水印 4
2.2 數位浮水印的分類 5
2.2.1 浮水印在視覺上的區分 6
2.2.2 浮水印在內容上的區分 6
2.2.3 浮水印在強健度上的區分 7
2.2.4 浮水印在嵌入方式上的區分 8
2.2.5 浮水印在截取方式上的區分 9
2.3 數位浮水印的特性 10
2.4 數位浮水印的應用 12
2.5 文獻探討 13
2.5.1 以離散餘弦轉換(DCT)為基礎的相關研究 13
2.5.2 以離散小波轉換(DWT)為基礎的相關研究 14
2.5.3 以奇異值分解(SVD)為基礎的相關研究 14
第三章 提出的浮水印方法 16
3.1 離散小波轉換 16
3.1.1 Haar轉換 17
3.1.2 一維離散小波轉換 18
3.1.3 二維離散小波轉換 19
3.2 以LATTICE CODES編碼的浮水印方法 22
3.2.1 Lattice Codes編碼 23
3.2.2 以Lattice Codes嵌入浮水印 24
3.2.3 以Lattice Codes取出浮水印 25
3.3 以JND做為小波係數門檻值的浮水印方法 26
3.3.1 JND失真察覺臨界值 27
3.3.2 以JND為門檻值嵌入浮水印 28
3.3.3 以JND為門檻值取出浮水印 30
3.4 以SVD做量化調變的浮水印方法 30
3.4.1 SVD轉換 30
3.4.2 以奇異值量化調變嵌入浮水印 31
3.4.3 以奇異值量化調變取出浮水印 33
3.5 ARNOLD轉換 33
3.6 浮水印的嵌入 34
3.7 浮水印的取出 35
3.8 浮水印的驗證 36
第四章 實驗結果 38
4.1 失真性壓縮的攻擊方式 40
4.1.1 第一種攻擊類型：JPEG失真壓縮 40
4.1.2 第二種攻擊類型：EZW失真壓縮 43
4.1.3 第三種攻擊類型：SPIHT失真壓縮 46
4.1.4 第四種攻擊類型：JPEG2000失真壓縮 49
4.2 常見影像處理的攻擊方式 51
4.2.1 第五種攻擊類型：平均濾波器 52
4.2.2 第六種攻擊類型：中值濾波器 53
4.2.3 第七種攻擊類型：高斯濾波器 55
4.2.4 第八種攻擊類型：銳化濾波器 56
4.2.5 第九種攻擊類型：直方圖等化 58
4.3 加入雜訊的攻擊方式 58
4.3.1 第十種攻擊類型：高斯雜訊 58
4.3.2 第十一種攻擊類型：胡椒鹽式雜訊 60
4.4 幾何攻擊方式 62
4.4.1 第十二種攻擊類型：縮放 62
4.4.2 第十三種攻擊類型：裁切 63
4.4.3 第十四種攻擊類型：旋轉 65
第五章 結論與未來研究方向 68
5.1結論 68
5.2未來研究方向 68
參考文獻 69
附 錄 72
A. 中英對照表 72

[1]N. Komatsu and H. Tominaga, “Authentication System Using Concealed Images in Telematics,” Memoirs of the School of Science and Engineering, Waseda University, 52:45-60, 1988.
[2] I. J. Cox, M. L. Miller, and J. A. Bloom, Digital watermarking, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 2002.
[3] I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure spread spectrum watermarking for multimedia,” IEEE Transactions on Image Processing, vol. 6, pp. 1673-1687, 1997.
[4] P. Yu and B. Liu, “Public Watermarking Algorithm Based on the Polarity of DCT Coefficients,” IEEE Proceedings of the 6th International Conference on Intelligent Systems Design and Applications, 2006.
[5] S. H. Wang and Y. P. Lin, “Wavelet Tree Quantization for Copyright Protection Watermarking,” IEEE Transactions on Image Processing, vol. 13, no. 2, pp. 154–165, February 2004.
[6] X. Niu, E. Li, and H. Liang, “Blind Image Watermarking Scheme Based on Wavelet Tree Quantization Robust to Geometric Attacks,” IEEE Proceedings of the 6th World Congress on Intelligent Control and Automation, Dalian, China, June 21-23 2006.
[7] P. Bao and X. Ma, “Image adaptive Watermarking Using Wavelet Domain Singular Value Decomposition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 15, no. 1, January 2005.

[8] Q. Li, C. Yuan and Y. Z. Zhong, “Adaptive DWT-SVD Domain Image Watermarking Using Human Visual Model,” February 12-14, ICACT2007.
[9] A. S. Lewis and G. Knowles ,”Image compression using the 2-D wavelet transform, ”IEEE Transactions on Image Processing, vol. 1, no. 2, pp. 244-250, Apr 1992.
[10]M. Bertran, J. F. Delaigle, and B. Macq, “Some improvements to HVS models for fingerprinting in perceptual decompressors,” in Proc. IEEE International Conference on Image Processing (ICIP''01), vol. 3, pp. 1039-1042, October 2001.
[11]G. Peterson, “Arnold’s Cat Map”, Linear Algebra, 1997
[12]R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley Publishing Company, 1993
[13]J. C Goswami and A. K. Chan, Fundamentals of Wavelets: Theory, Algorithms, and Applications, Wiley, 1999.
[14]J. Cox, M. L. Miller, and A. McKellips, “Watermarking as communications with side information,” Proceedings of the IEEE, vol. 87, no. 7, pp. 1127–1141, Jul. 1999.
[15]J. Chou, S. S. Pradhan, and K. Ramchandran, “On the Duality Between Distributed Source Coding and Data Hiding,” Thirty-third Asilomar Conference on Signals, Systems, and Computers, 2:1503-1507, 1999.
[16]B. Chen and G. W. Wornell, “An Information-theoretic Approach to the Design of Roust Digital Watermarking Systems,” IEEE Conference on Acustics, Speech, and Signal Process, vol. 4, pp. 2061-2064, 1999.
[17]B. Chen and G. W. Wornell. “Digital Watermarking and Information Embedding Using Dither Modulation,” in IEEE Second Workshop on Multimedia Signal Processing, pp. 273-278, 1998.
[18]H. R. Wilson and G. C. Phillips, “Orientation Bandwidths of Spatial Mechanisms Measured by Masking,” J. Opt. Soc. Am. A, vol. 1, no. 2, pp. 226-232, Feb 1984.
[19]G. Buchsbaum, “An analytical derivation of visual nonlinearity,” IEEE Transactions on biomedical engineering, vol. 27, pp. 237-242, 1980
[20]A. B. Watson and J. A. Solomon, “A model of visual contrast gain control and pattern masking,” Journal of Optical Society of America, pp. 2377-2391, 1997.
[21]V. I. Gorodetski, L. J. Popyack, and V. Samoilov, “SVD-base approach to transparent embedding data into digital image,” in Proc. Int. workshop, MMM-ACNS, St. Petersburg, Russia, pp. 263-274, May 2001.
[22]R. Liu and T. Tan, “An SVD-based watermarking scheme for protecting rightful ownership,” IEEE Transactions on Multimedia, vol. 4, no. 1, pp. 121-128, Mar 2002.
[23]A. Shnayderman, A. Gusev, and A. M. Eskicioglu, “An SVD-based grayscale image quality measure for local and global assessment,” IEEE Transactions on Image Processing,” vol. 15 no. 2, pp. 422-429, Feb 2006.
[24]D. Kundur and D. Hatzinakos, “Digital watermarking, using multiresolution wavelet decomposition,” in Proc. IEEE ICASSP, vol. 5, pp. 2969-2972, 1998.
[25]J. M. Shapiro, “Embedded Image Coding Using Zerotrees of Wavelet Coefficients,” IEEE Transactions on Signal Processing, vol. 41, pp. 3445-3462, Dec 1993.
[26]A. Said and W. A. Pearlman, “A New, Fast and Efficient Image Codec Based on Set Partitioning In Hierarchical Trees,” IEEE Transactions on Circuits system for Video Technology, vol. 6, pp. 243-250, Jun 1996.
[27]D. Taubman and A. Zakhor, “Multi-rate 3-D subband coding of video,” IEEE Transactions on Image Processing, vol. 3, no. 5, pp. 572-588, Sep 1994.