臺灣博碩士論文加值系統

網際網路的發達，使得聲音、影像及影片等多媒體資料容易被取得，甚至於遭到有意及無意的破壞與盜用。為了使多媒體資料原創者著作權受到保護，不論影像遭到旋轉、縮放及位移處理後，仍可以準確辨識該影像著作權，本研究以適用於浮水印之尺度(Scale)不變特徵值轉換，擷取影像中較穩定的特徵值，並分別在影像空間域及頻率域上，配合浮水印量化及中頻係數選取，在特徵值周圍進行嵌入與萃取浮水印訊息，以有效抵抗RST攻擊。
實驗結果顯示，影像經過旋轉(Rotation)攻擊，例如旋轉30度以上，或是受到放大2倍以上之縮放(Scaling)攻擊，其整體浮水印萃取效能(Performance)為75%及89%。從結果可知，不論在空間域或是頻率域浮水印技術，我們都可使浮水印萃取之效能較為強韌。

Traditional watermarking schemes are sensitive to geometric distortions, in which synchronization for recovering the embedded information is challenging due to the disorder caused from rotation, scaling or translation (RST). This thesis presents robust watermarking algorithms based on SIFT features, which are resilient to geometric attacks, including RST, and even warping. By embedding watermark bits in RST-invariant keypoints in the image and aligning the embedding direction according to the principle axis, watermark can be well preserved and extracted even if the image is destroyed by RST. Since limited robust features can be found in the spatial domain, deriving a robust spatial-domain watermarking is another difficult problem. However, besides the frequency-domain watermarking schemes, we also design a quantization watermarking scheme in the spatial domain, which is robust to RST. Experiment results show the proposed algorithms are superior to many existing works.

附表目錄 vii
附圖目錄 viii
第一章 緒論 1
1.1 研究背景 1
1.2 研究目的 3
1.3 問題闡述 3
1.4 研究範圍 5
1.5 論文組織架構 6
第二章 相關文獻探討 7
2.1空間域浮水印技術 7
2.2頻率域浮水印技術 8
2.3植基於特徵點浮水印技術 9
第三章 研究方法 13
3.1空間域浮水印技術 14
3.1.1決定AOI範圍 15
3.1.2 RST-invariant特徵值擷取 16
3.1.3主軸校正 20
3.1.4浮水印嵌入 24
3.1.5浮水印萃取 25
3.2 頻率域浮水印技術 27
3.2.1決定AOI範圍 28
3.2.2 RST-invariant特徵值擷取 28
3.2.3主軸校正 29
3.2.4浮水印嵌入 29
3.2.5浮水印萃取 30
3.3 計算原始浮水印及萃取後浮水印相似度 30
第四章 實驗結果 32
4.1 影像品質 36
4.2 空間域實驗結果 38
4.2.1 旋轉攻擊 38
4.2.2 縮放攻擊 39
4.2.3 位移攻擊 40
4.2.4 切割攻擊 41
4.2.5 扭曲攻擊 42
4.2.6 所有攻擊 43
4.2.7實驗分析 45
4.3 頻率域 46
4.3.1 旋轉攻擊 46
4.3.2 縮放攻擊 46
4.3.3 位移攻擊 47
4.3.4 切割攻擊 48
4.3.5 扭曲攻擊 48
4.3.6 所有攻擊 49
4.3.7實驗分析 51
4.4 浮水印方法比較 52
4.4.1 各種攻擊比較 52
4.4.2實驗分析 53
第五章 結論 55
5.1 結論 55
5.2 未來展望 56
參考文獻 57

[1] P. Bas, J. M. Chassery, and B. Macq, “Geometrically Invariant Watermarking Using Feature Points,” IEEE Trans. Image Process., vol. 11, no. 9, pp. 1014-1028, Sep. 2002.
[2] S. C. Byun, S. K. Lee, A. H. Tewfik, and B. H. Ahn, “A SVD-Based Fragile Watermarking Scheme for Image Authentucation,” IWDW 2002, vol. 2613, pp. 170-178, 2003.
[3] Checkmark Benchmark 1.2, http://watermarking.unige.ch/Checkmark/
[4] I. J. Cox, J. Kilian, F. T. Leighton, and T. Shamoon, “Secure Spread Spectrum Watermarking for Multimedia,” IEEE Trans. Image Process., vol. 6, no. 12, pp. 1673-1687, Dec. 1997.
[5] P. Dong, J. G. Brankov, N. P. Galatsanos, Y. Yang , and F. Davoine, “Digital Watermarking Robust to Geometric Distortions, ” IEEE Trans. Image Process., vol. 14, no. 12, pp. 2140 -2150, Dec. 2005.
[6] E. Ganic and A. M. Eskicioglu, “Robust DWT-SVD Domain Image Watermarking: Embedding Data in All Frequencies, ” MM&SEC'04, pp. 20 -21, Sep. 2004.
[7] J. R. Hernandez, M. Amado, and F. P. Gonzalez, “DCT-Domain Watermarking Techniques for Still Images: Detector Performance Analysis and a New Structure, ” IEEE Trans. Image Process., vol. 9, no. 1, pp. 55 -68, Jan. 2000.
[8] http://en.wikipedia.org/wiki/Wavelet#Mother_wavelet
[9] C. T. Hsu and J. L. Wu, “Hidden Digital Watermarks in Images,” IEEE Trans. Image Process., vol. 8, no. 1, pp. 58 -68, Jan. 1999.
[10] J. Huang, Y. Q. Shi, and Y. Shi, “Embedding Image Watermarks in DC Components,” IEEE Trans. Circuits and Systems for Video Technology, vol. 10, no. 6, pp. 974 -979, Sep. 2000.
[11] S. S. Jin, and D. Y. Chang, “Image Watermarking Based on Invariant Regions of Scale-Space Representation“, IEEE Trans. Signal Process., vol. 54, no. 4, pp. 1537 -1549, Apr. 2006.
[12] J. Kaufman and M. Celenk, “Digital Video Watermarking using Singular Value Decomposition and 2D Principal Component Analysis,” IEEE International Conference on Image Processing, pp. 2561-2564, Oct. 2006.
[13] G. C. Langelaar and R. L. Lagendijk, “Optimal Differential Energy Watermarking of DCT Encoded Images and Video, ” IEEE Trans. Image Process., vol. 10, no. 1, pp. 148 -157, Jan. 2001.
[14] H. Y. Lee, H. Kim, and H.-K. Lee, “Robust Image Watermarking using Local Invariant Features, ” Optical Engineering, SPIE 2006, vol. 45, no. 3, March 2006
[15] C. Y. Lin, M. Wu, J. A. Bloom, I. J. Cox, M. Miller, and Y. M. Lui, “Rotation, Scale, and Translation Resilient Public Watermarking for Images,” IEEE Trans. Image Process., vol. 10, no. 5, pp. 767-782, May 2001.
[16] T. Lindeberg, “Scale-Space Theory: A Basic Tool for Analysing Structures at Different Scales,” Journal of Applied Statistics, vol. 21, no. 2, pp. 224-270, 1994.
[17] H. Liu, B. Guo, and Z. Feng , “Pseudo-Log-Polar Fourier Transform for Image Registration,” IEEE Signal Process. Letters, vol. 13, no. 1, pp.17-20, Jan. 2006.
[18] Y. Liu and J. Zhao, “A Robust RST Invariant Image Watermarking Method Based on Locally Detected Features,” IEEE International Workshop on Haptic Audio Visual Environments and their Applications, Oct. 2005.
[19] R. Liu and T. Tan, “An SVD-Based Watermarking Scheme for Protecting Rightful Ownership,” IEEE Trans. Multimedia, vol. 4, no. 1, pp. 121-128, Mar. 2002.
[20] D. G. Lowe, “Object Recognition from Local Scale-Invariant Feature,” Proc. Of the International Conference on Computer Vision, vol. 2, pp.1150-1157, Sep. 1999.
[21] D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, Nov. 2004.
[22] A. Masoud and H. T. Ahmed, “Geometric Invariance in Image Watermarking,” IEEE Trans. Image Process., vol. 13, no. 2, pp. 145 -153, Feb. 2004.
[23] K. Mikolajczyk and C. Schmid, “Scale and Affine Invariant Interest Point Detectors,” Int. J. Comput. Vis., vol. 60, no. 1, pp. 63-86, 2004
[24] S. C. Pei and C. N. Lin, “Image Normalization for Pattern Recognition, ”Image and Vision Computing, vol. 13, no. 10, pp. 711-723, Dec. 1995.
[25] V. Q. Pham, T. Miyaki, T. Yamasaka, and K. Aizawa, “Geometrically Invariant Object-based Watermarking using SIFT Feature”, IEEE International Conference on Image Processing ,ICIP 2007, vol. 5, pp. 473-476, Oct. 2007.
[26] L. K. Shark and C. Yu, “Design of Matched Wavelets Based on Generalized Mexican-Hat Function,” Signal Processing , vol. 86, no. 7, pp. 1451-1469, July 2006.
[27] C. W. Tang and H. M. Hang, “A Feature-Based Robust Digital Image Watermarking Scheme,” IEEE Trans. Image Process., vol. 51, no. 4, pp. 950-959, Apr. 2003.
[28] L. Vinicius and J. Ramiro, “Geometric Attacks on Image Watermarking Systems”, Multimedia, IEEE, vol. 12, no. 3, pp. 68 -78, Jul.-Sep. 2005
[29] X. Y. Wang, L. M. Hou, and J. Wu, “A Feature-based Robust Digital Image Watermarking against Geometric Attacks”, Image and Vision Computing, vol. 26, no. 7, pp. 980-989, July 2008.
[30] S. Xiang, H. J. Kim, and J. Huang, “Invariant Image Watermarking Based on Statistical Features in the Low-Frequency Domain,“ IEEE Trans. Circuits and Systems for Video Technology, vol. 18, no. 6, pp. 777-790, June 2008.
[31] K. Yosi, A. Amir, and I. Moshe, “Pseudopolar-Based Estimation of Large Translations, Rotations, and Scalings in Images,” IEEE Trans. Image Process., vol. 14, no. 1, pp. 12 -22, Jan. 2005.
[32] D. Zheng, J. Zhao, and A. El Saddik, “RST Invariant Digital Image Watermarking Based on Log-Polar Mapping and Phase Correlation,” IEEE Trans. Circuits and Systems for Video Technology, vol. 13, no.8, pp.753-765, Sep. 2003.
[33] D. Zheng, Y. Liu, J. Zhao, and El Saddik, “A Survey of RST Invariant Image Watermarking Algorithms, ” ACM Computing Surveys , vol. 39, no. 2, article 5, Jun. 2007.
[34] S. Zheng, Y. Zhu, and X. Wang, “A New RST-Invariant Watermarking Scheme Based on Texture Features, ” ICST 2008, no. 23, pp. 21 -23, Jan. 2008.
[35] http://berlin.csie.ntnu.edu.tw/Courses/Speech%20Recognition/Lectures2009/SP2009F_Lecture02_HiddenMarkovModel.pdf
[36] 潘正祥, 張真誠及林詠章,“挑戰影像處理-數位浮水印技術,” 美商麥格羅希爾國際股份有限公司, 2007/12/01.