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基于圖像獨立特征分解的數(shù)字水印方法

劉琚,  孫建德

劉琚, 孫建德. 基于圖像獨立特征分解的數(shù)字水印方法[J]. 電子與信息學報, 2003, 25(9): 1174-1179.
引用本文: 劉琚, 孫建德. 基于圖像獨立特征分解的數(shù)字水印方法[J]. 電子與信息學報, 2003, 25(9): 1174-1179.
Liu Ju, Sun Jiande. AN APPROACH OF WATERMARKING BASED ON INDEPENDENT FEATURE DECOMPOSITION OF IMAGE[J]. Journal of Electronics & Information Technology, 2003, 25(9): 1174-1179.
Citation: Liu Ju, Sun Jiande. AN APPROACH OF WATERMARKING BASED ON INDEPENDENT FEATURE DECOMPOSITION OF IMAGE[J]. Journal of Electronics & Information Technology, 2003, 25(9): 1174-1179.

基于圖像獨立特征分解的數(shù)字水印方法

AN APPROACH OF WATERMARKING BASED ON INDEPENDENT FEATURE DECOMPOSITION OF IMAGE

  • 摘要: 獨立分量分析(ICA)是在研究盲源分離過程中出現(xiàn)的一種全新的信號處理和數(shù)據(jù)分析方法。利用ICA方法,可以在不知源信號和傳輸通道的參數(shù)的情況下,根據(jù)輸入源信號的統(tǒng)計特性,僅通過觀測信號就能實時地恢復或提取源信號。該文把圖像看成是多個獨立的特征圖像的混疊。利用獨立分量分析方法同時對數(shù)字圖像和水印圖像進行獨立特征分析,得到一種新的數(shù)字水印方法。計算機實驗的結果表明這種方法加入的數(shù)字水印可以被恢復,并且具有一定的魯棒性。
    Abstract: Independent Component Analysis (ICA), which is presented in studying the prob-lem of blind source separation, is a novel method of signal processing and data analysis. Using ICA, the source signals can be extracted only from the observations according to the statistic independent characteristics of the input signals, even without the coefficients of the source sig-nals and the transfer channels. In this paper, an image is considered as a mixture of several independent feature causes, and the ICA is used both on the original image and the watermark image respectively, A new approach of embodying digital watermark is proposed. The simu-lation results show that the embedded digital watermark can be extracted correctly, and the robustness exists in the new approach.
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  • F.A.P. Petitcolas, R. J. Anderson, M. G. Kuhn, Information hiding, A survey, Proc. IEEE,1999, 87(7), 1062-1078.[2]V. Cappellini, M. Barni, F. Bartolini, A. Piva, A DCT-domain system for robust image watermarking, Signal Processing, 1998, 66(3), 357-372.[3]M.D. Swanson, B. Zhu, et al., Transparent robust image watermarking, Proc. IEEE ICIP, 1996,Lausanne, Switzerland, 3, 211-214.[4]J.R. Hernandez, F. P. Perez-Gonzalez, Statistical analysis of watermarking schemes for copyright protection of images, Proc. IEEE, 1999, 87(7), 1142-1166.[5]W. Bender, D. Druhl, et al., Techniques for data hiding, IBM Syst. J., 1996, 35(3/4), 313-316.[6]F.J. Gonzalez-Serrano, et al., Independent component analysis applied to digital image watermarking, in Proc. of ICASSP2001, Salt Lake City, Utah, 2001, 1997-2000.[7]Ju Liu, Kaibao Nie, Zhenya He, Blind separation by redundancy reduction in a recurrent neural network, Chinese Journal of Electronics, 2001, 10(3), 415-419.[8]He Zhenya, Liu Ju, Yang Luxi, Blind separation of images using edgeworth expansion based ICA algorithm, Chinese Journal of Electronics, 1999, 8(3), 278-282.[9]P. Comon, Independent component analysis, a new concept? Signal Processing, 1994, 36(3),287-314.[10]A.J. Bell, T. J. Sejnowski, An information-maximization approach to blind separation and blind deconvolution, Neural Computation, 1995, 7(6), 1129-1159.[11]S. Amari, A. Cichocki, H. H. Yang, A new learning algorithm for blind signal separation, in Advances in Neural Information Processing Systems, Cambridge, MA, MIT Press, 1996, 657-663.[12]A.J. Bell, T. J. Sejnowski, Edges are the independent components of natural scenes, in Advances in Neural Information Processing Systems, Cambridge, MA, MIT Press, 1996, 9, 831-837.
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出版歷程
  • 收稿日期:  2002-04-22
  • 修回日期:  2002-11-18
  • 刊出日期:  2003-09-19

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