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基于分块压缩感知的图像半脆弱零水印算法

DOI: 10.3724/SP.J.1004.2012.00609, PP. 609-617

Keywords: 半脆弱水印,零水印,压缩感知,篡改定位,图像恢复

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Abstract:

?针对数字图像的内容认证和完整性保护问题,提出了一种基于分块压缩感知(Compressivesensing,CS)的图像半脆弱零水印算法(Blockcompressivesensingbasedimagesemi-fragilezero-watermarking,BCS-SFZ).首先将图像划分成若干分块,分块大小可以根据水印数据量和篡改定位精度调整.再按照压缩感知理论对各个图像块进行观测,并将观测值作为零水印信息注册保存.实验结果表明,BCS-SFZ算法可以准确定位非法篡改并借助水印信息恢复被篡改的区域.压缩感知理论的引入为算法提供了保密性支持,并且有利于实现图像成像与水印生成的同步,同时该算法实现简单,计算复杂度低.

References

[1]  Zhang Li-Bao, Ma Xin-Yue, Chen Qi. Image zero-watermarking algorithm based on region of interest. Journal on Communications, 2009, 30(S2): 117-120(张立保, 马新悦, 陈琪. 基于感兴趣区的图像零水印算法. 通信学报, 2009, 30(S2): 117-120)
[2]  Wen Quan, Sun Tan-Feng, Wang Shu-Xun. Concept and application of zero-watermark. Acta Electronica Sinica, 2003, 31(2): 214-216(温泉, 孙锬锋, 王树勋. 零水印的概念与应用. 电子学报, 2003, 31(2): 214-216)
[3]  Candes E J, Wakin M B. An introduction to compressive sampling: a sensing/sampling paradigm that goes against the common knowledge in data acquisition. IEEE Signal Processing Magazine, 2008, 25(2): 21-30
[4]  Rachlin Y, Baron D. The secrecy of compressed sensing measurements. In: Proceedings of the 46th Annual Allerton Conference on Communication, Control and Computing. Illinois, USA: IEEE, 2008. 813-817
[5]  Baraniuk R, Davenport M, DeVore R, Wakin M. A simple proof of the restricted isometry property for random matrices. Constructive Approximation, 2008, 28(3): 253-263
[6]  Dossal C, Peyre G, Fadili J. A numerical exploration of compressed sampling recovery. Linear Algebra and Its Applications, 2010, 432(7): 1663-1679
[7]  Duarte M, Davenport M, Takhar D, Laska J N, Sun T, Kelly K F, Baraniuk R G. Single-pixel imaging via compressive sampling. IEEE Signal Processing Magazine, 2008, 25(2): 83-91
[8]  Niu Xia-Mu, Jiao Yu-Hua. An overview of perceptual Hashing. Acta Electronica Sinica, 2008, 36(7): 1405-1411(牛夏牧, 焦玉华. 感知哈希综述. 电子学报, 2008, 36(7): 1405-1411)
[9]  Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306
[10]  Romberg J. Imaging via compressive sampling. IEEE Signal Processing Magazine, 2008, 25(2): 14-20
[11]  Donoho D L. For most large underdetermined systems of equations, the minimal L_{1}-norm near-solution approximates the sparsest near-solution. Communications on Pure and Applied Mathematics, 2006, 59(7): 907-934
[12]  Candes E, Romberg J. Quantitative robust uncertainty principles and optimally sparse decompositions. Foundations of Computational Mathematics, 2006, 6(2): 227-254
[13]  Gan L. Block compressed sensing of natural images. In: Proceedings of the15th International Conference on Digital Signal Processing. Cardiff, UK: IEEE, 2007. 403-406

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