當嵌入數位浮水印的靜態影像遭到例如旋轉、裁切、縮放，甚至是隨機變形等幾何攻擊時，經常造成數位浮水印偵測的失敗。本研究提出了基於特徵點擷取之強健型數位浮水印方法，來抵抗幾何變形攻擊所產生的同步問題。首先，我們利用尺度不變特徵轉換(Scale-space Invariant Feature Transform)演算法來擷取特徵點作為定位，依據此特徵點位置延伸出大量的局部不變區域，在每個局部不變區域中嵌入浮水印訊號，接著再嵌入解決同步問題的樣板訊號。較大的偵測區域使得浮水印嵌入量獲得提升，並且提高偵測的可信度。在偵測隱藏訊號時，由於影像可能遭受各種攻擊，導致特徵點資訊與原先不同。因此，我們在使用SIFT擷取特徵點後，對於每個特徵點所建構的不變區域參數進行微調整，以尋找最佳的可能嵌入區域。利用延伸樣板解決同步問題後，我們即可從中擷取出數位浮水印資訊。實驗結果顯示我們所提出的浮水印方法對於各種不同的幾何攻擊與訊號處理攻擊，皆具有合理的強健性。

Synchronized watermark detection is an important issue. The embedded watermark may not be detected successfully if the image has undergone such geometrical transformations as rotation, cropping, scaling or even random bending. This research presents a feature-based still image watermarking approach. Scale-Invariant Feature Transform (SIFT) is first applied to locate the interest points, from which we form the invariant regions for watermark embedding. To resist geometrical transformations, the extended synchronization templates, which help to ensure that reasonably large invariant regions will be available for carrying the watermark payload and/or for increasing the confidence of watermark detection, will also be embedded. In the detection phase, after SIFT, the template is first determined locally by adjusting the related affine parameters of the grid to match with the possible hidden template signal so that the watermark can be retrieved afterwards. Experimental results show the feasibility of the proposed method.

[1] I. Cox, M. Miller, J. Bloom, J. Fridrich, and T. Kalker, "Digital Watermarking and Steganography," 2007.
[2] M. Barni, "Effectiveness of exhaustive search and template matching against watermark desynchronization," Signal Processing Letters, IEEE, vol. 12, pp. 158-161, 2005.
[3] J. J. K. Ruanaidh and T. Pun, "Rotation, scale and translation invariant spread spectrum digital image watermarking1," Signal processing, vol. 66, pp. 303-317, 1998.
[4] C. Y. Lin, M. Wu, J. A. Bloom, I. J. Cox, M. L. Miller, and Y. M. Lui, "Rotation, scale, and translation resilient watermarking for images," Image Processing, IEEE Transactions on, vol. 10, pp. 767-782, 2001.
[5] D. Zheng, J. Zhao, and A. El Saddik, "RST-invariant digital image watermarking based on log-polar mapping and phase correlation," Circuits and Systems for Video Technology, IEEE Transactions on, vol. 13, pp. 753-765, 2003.
[6] S. Pereira and T. Pun, "Robust template matching for affine resistant image watermarks," Image Processing, IEEE Transactions on, vol. 9, pp. 1123-1129, 2000.
[7] M. Kutter, G. Teschar Andrew, B. Vasudev, V. M. Bove, and B. Derryberry, "Watermarking resisting to translation, rotation, and scaling," SPIE proceedings series, vol. 3528, pp. 423-431, 1999.
[8] P. C. Su and C. C. J. Kuo, "Synchronized detection of the block-based watermark with invisible grid embedding," in SPIE Photonics West, vol. 4314, pp. 423–431, 2001.
[9] M. Kutter, S. K. Bhattacharjee, and T. Ebrahimi. (1999, 1999) Towards second generation watermarking schemes. Image Processing, 1999. ICIP 99. Proceedings. 1999 International Conference on. 320-323 vol.1.
[10] H. Alexander, V. Sviatoslav, R. Yuriy, and W. Ping Wah, "The watermark template attack," SPIE proceedings series, vol. 4314, pp. 394-405, 2001.
[11] S. Manjunath B, C. Shekhar, and R. Chellappa, "A new approach to image feature detection with applications," Pattern recognition, vol. 29, pp. 627-640, 1996.
[12] P. Bas, J. M. Chassery, and B. Macq, "Geometrically invariant watermarking using feature points," Image Processing, IEEE Transactions on, vol. 11, pp. 1014-1028, 2002.
[13] J. S. Seo and C. D. Yoo, "Image watermarking based on invariant regions of scale-space representation," Signal Processing, IEEE Transactions on, vol. 54, pp. 1537-1549, 2006.
[14] G. Xinbo, D. Cheng, L. Xuelong, and T. Dacheng, "Geometric Distortion Insensitive Image Watermarking in Affine Covariant Regions," Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 40, pp. 278-286, 2010.
[15] W. Xiangyang, W. Jun, and N. Panpan, "A New Digital Image Watermarking Algorithm Resilient to Desynchronization Attacks," Information Forensics and Security, IEEE Transactions on, vol. 2, pp. 655-663, 2007.
[16] Z. Dong, W. Sha, and Z. Jiying, "RST Invariant Image Watermarking Algorithm With Mathematical Modeling and Analysis of the Watermarking Processes," Image Processing, IEEE Transactions on, vol. 18, pp. 1055-1068, 2009.
[17] T. Jen-Sheng, H. Win-Bin, and K. Yau-Hwang, "On the Selection of Optimal Feature Region Set for Robust Digital Image Watermarking," Image Processing, IEEE Transactions on, vol. 20, pp. 735-743, 2011.
[18] C. Harris and M. Stephens, "A combined corner and edge detector," in Alvey vision conference, vol. 15, p. 50, 1988.
[19] G. Lowe D, "Distinctive image features from scale-invariant keypoints," International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.
[20] K. Mikolajczyk and C. Schmid, "Scale & affine invariant interest point detectors," International Journal of Computer Vision, vol. 60, pp. 63-86, 2004.
[21] P. F. A. P, S. Martin, R. Frederic, D. Jana, F. Caroline, F. Nazim, and W. Ping Wah, "A public automated web-based evaluation service for watermarking schemes: StirMark benchmark," SPIE proceedings series, vol. 4314, pp. 575-584, 2001.
[22] A. B. Watson, "Visually optimal DCT quantization matrices for individual images," in Data Compression Conference, 1993. DCC ''93., 1993, pp. 178-187.
[23] S. Voloshynovskiy, A. Herrigel, N. Baumgaertner, and T. Pun, "A stochastic approach to content adaptive digital image watermarking," in Information Hiding, pp. 211-236, 2000.