基于图像独立特征分解的数字水印方法

刘琚; 孙建德

基于图像独立特征分解的数字水印方法

刘琚,
孙建德

计量
- 文章访问数: 2222
- HTML全文浏览量: 117
- PDF下载量: 533
- 被引次数: 0
出版历程
- 收稿日期: 2002-04-22
- 修回日期: 2002-11-18
- 刊出日期: 2003-09-19

AN APPROACH OF WATERMARKING BASED ON INDEPENDENT FEATURE DECOMPOSITION OF IMAGE

Liu Ju,
Sun Jiande

摘要

摘要: 独立分量分析(ICA)是在研究盲源分离过程中出现的一种全新的信号处理和数据分析方法。利用ICA方法,可以在不知源信号和传输通道的参数的情况下,根据输入源信号的统计特性,仅通过观测信号就能实时地恢复或提取源信号。该文把图像看成是多个独立的特征图像的混叠。利用独立分量分析方法同时对数字图像和水印图像进行独立特征分析,得到一种新的数字水印方法。计算机实验的结果表明这种方法加入的数字水印可以被恢复,并且具有一定的鲁棒性。
- 独立分量分析; 特征提取; 数字水印
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.

HTML全文

参考文献(1)

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.

施引文献

资源附件(0)

访问统计