2.765

2022影响因子

(CJCR)

  • 中文核心
  • EI
  • 中国科技核心
  • Scopus
  • CSCD
  • 英国科学文摘

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于分块压缩感知的图像半脆弱零水印算法

赵春晖 刘巍

赵春晖, 刘巍. 基于分块压缩感知的图像半脆弱零水印算法. 自动化学报, 2012, 38(4): 609-617. doi: 10.3724/SP.J.1004.2012.00609
引用本文: 赵春晖, 刘巍. 基于分块压缩感知的图像半脆弱零水印算法. 自动化学报, 2012, 38(4): 609-617. doi: 10.3724/SP.J.1004.2012.00609
ZHAO Chun-Hui, LIU Wei. Block Compressive Sensing Based Image Semi-fragile Zero-watermarking Algorithm. ACTA AUTOMATICA SINICA, 2012, 38(4): 609-617. doi: 10.3724/SP.J.1004.2012.00609
Citation: ZHAO Chun-Hui, LIU Wei. Block Compressive Sensing Based Image Semi-fragile Zero-watermarking Algorithm. ACTA AUTOMATICA SINICA, 2012, 38(4): 609-617. doi: 10.3724/SP.J.1004.2012.00609

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

Block Compressive Sensing Based Image Semi-fragile Zero-watermarking Algorithm

  • 摘要: 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(Compressive sensing, CS)的图像 半脆弱零水印算法(Block compressive sensing based image semi-fragile zero-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.
  • [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] 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)[3] 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)[4] Donoho D L. Compressed sensing. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306[5] 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[6] Romberg J. Imaging via compressive sampling. IEEE Signal Processing Magazine, 2008, 25(2): 14-20[7] 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[8] 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[9] 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[10] Candes E, Romberg J. Quantitative robust uncertainty principles and optimally sparse decompositions. Foundations of Computational Mathematics, 2006, 6(2): 227-254[11] Dossal C, Peyre G, Fadili J. A numerical exploration of compressed sampling recovery. Linear Algebra and Its Applications, 2010, 432(7): 1663-1679[12] Gan L. Block compressed sensing of natural images. In: Proceedings of the15th International Conference on Digital Signal Processing. Cardiff, UK: IEEE, 2007. 403-406[13] 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
  • 加载中
计量
  • 文章访问数:  2052
  • HTML全文浏览量:  53
  • PDF下载量:  1086
  • 被引次数: 0
出版历程
  • 收稿日期:  2011-03-04
  • 修回日期:  2011-08-30
  • 刊出日期:  2012-04-20

目录

    /

    返回文章
    返回