电子学报 ›› 2012, Vol. 40 ›› Issue (4): 681-687.DOI: 10.3969/j.issn.0372-2112.2012.04.010

• 学术论文 • 上一篇    下一篇

基于压缩感知的交互支持双水印算法

赵春晖, 刘巍   

  1. 哈尔滨工程大学信息与通信工程学院,黑龙江哈尔滨 150001
  • 收稿日期:2011-03-02 修回日期:2012-02-19 出版日期:2012-04-25 发布日期:2012-04-25

Mutual Support Dual Watermark Algorithm Based on Compressive Sensing

ZHAO Chun-hui, LIU Wei   

  1. College of Information and communication Engineering,Harbin Engineering University,Harbin,Heilongjiang 150001,China
  • Received:2011-03-02 Revised:2012-02-19 Online:2012-04-25 Published:2012-04-25

摘要: 针对一般水印算法功能单一,而双水印算法中两种水印互相干扰的问题,提出了一种交互支持双水印算法.首先将鲁棒水印嵌入图像中,然后从鲁棒水印的密钥中抽取出一部分形成观测矩阵,使用该观测矩阵对图像进行分块压缩感知(Compressive Sensing,CS),观测值即为半脆弱水印,将半脆弱水印作为零水印注册保存.零水印的使用减少了双水印对原始图像视觉效果的影响,可以有效避免两种水印之间的干扰.压缩感知理论的引入实现了两种水印之间的交互支持,一方面,鲁棒水印为半脆弱水印的生成提供观测矩阵及保密支持,另一方面半脆弱水印可以增强鲁棒水印的性能并验证其密钥的真实性.

关键词: 数字水印, 压缩感知, 双水印, 零水印, 奇异值分解

Abstract: Watermarking algorithms in general only have single function,and dual watermark algorithms have interference problems between the two watermarks.To address these issues,this paper proposes a mutual support dual watermark algorithm.Firstly embed robust watermark in the image,and then divided the image into blocks,extract measurement matrixes from the key of the robust watermark and observe each image block using these matrixes in accordance with the compressive sensing (CS) theory,the measurements are the semi-fragile watermark which will be registered as a zero-watermarking.The use of zero-watermarking can reduce the impact of visual effect on the original image by dual watermark,and effectively avoid the interference problems.The introduction of CS theory realizes the interaction between the two watermarks.On the one hand,the robust watermark provide measurement matrixes and secrecy support for the semi-fragile watermark,on the other hand,the semi-fragile watermark can Enhance the robust performance of the robust watermark and verify the authenticity of its key.

Key words: digital watermark, compressive sensing (CS), dual watermark, zero-watermarking, singular value decomposition (SVD)

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