ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2019, Vol. 56 ›› Issue (7): 1454-1469.doi: 10.7544/issn1000-1239.2019.20180278

• 信息安全 • 上一篇    下一篇

基于二元Weibull分布的非下采样Shearlet域图像水印算法

牛盼盼1,2,王向阳1,杨思宇1,文涛涛1,杨红颖1   

  1. 1(辽宁师范大学计算机与信息技术学院 辽宁大连 116029);2(大连理工大学电子信息与电气工程学部 辽宁大连 116023) (niupanpan3333@163.com)
  • 出版日期: 2019-07-01
  • 基金资助: 
    国家自然科学基金项目(61472171,61701212);中国博士后科学基金项目(2017M621135,2018T110220);大连市高层次人才创新支持计划项目(2017RQ055)

A Blind Watermark Decoder in Nonsubsampled Shearlet Domain Using Bivariate Weibull Distribution

Niu Panpan1,2, Wang Xiangyang1, Yang Siyu1, Wen Taotao1, Yang Hongying1   

  1. 1(School of Computer and Information Technology, Liaoning Normal University, Dalian, Liaoning 116029);2(Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, Liaoning 116023)
  • Online: 2019-07-01

摘要: 不可感知性、鲁棒性、水印容量是衡量数字图像水印算法优劣的最重要指标,且三者存在固有的相互矛盾关系,可保持不可感知性、鲁棒性、水印容量之间良好平衡的图像水印方法研究是一项富有挑战性的工作.以非下采样Shearlet变换(nonsubsampled Shearlet transform, NSST)与二元Weibull分布理论为基础,提出了一种基于二元Weibull统计建模的非下采样Shearlet域数字图像水印算法.1)构造出基于非线性单调函数的自适应高阶水印嵌入强度函数;2)根据NSST域尺度间相关性,利用二元Weibull边缘分布对NSST域高熵块奇异值进行统计建模,并估计出二元Weibull统计模型参数;3)结合NSST域二元Weibull边缘分布模型与最大似然决策理论,构造出二元数字水印检测器并盲提取水印信息.仿真实验结果表明:该算法可以较好地获得不可感知性、鲁棒性、水印容量之间的良好平衡.

关键词: 图像水印, 二元Weibull分布, 非下采样Shearlet变换, 最大似然决策, 相关性

Abstract: Digital image watermarking has become a necessity in many applications such as data authentication, broadcast monitoring on the Internet and ownership identification. There are three indispensable, yet contradictory requirements for a watermarking scheme: perceptual transparency, watermark capacity, and robustness against attacks. Therefore, a watermarking scheme should provide a trade-off among these requirements from the information-theoretic perspective. Improving the ability of imperceptibility, watermark capacity, and robustness at the same time has been a challenge for all image watermarking algorithms. In this paper, we propose a novel digital image watermark decoder in the nonsubsampled Shearlet transform (NSST) domain, wherein a PDF (probability density function) based on the bivariate Weibull distribution is used. In the presented scheme, we construct the nonlinear monotone function based adaptive high-order watermark embedding strength functions by employing the human visual system (HVS) properties, and embed watermark data into the singular values of high entropy NSST coefficients blocks. At the watermark receiver, the singular values of high entropy NSST coefficients blocks are firstly modeled by employing the bivariate Weibull distribution according to their inter-scale dependencies, then the statistical model parameters of bivariate Weibull distribution are estimated effectively, and finally a blind watermark extraction approach is developed using the maximum likelihood method based on the bivariate Weibull distribution. The experimental results show that the proposed blind watermark decoder is superior to other decoders in terms of imperceptibility and robustness.

Key words: image watermarking, bivariate Weibull distribution, nonsubsampled Shearlet transform (NSST), maximum likelihood decision, dependencies

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