基于奇异值分解和条件异方差的数字水印算法
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摘要
针对基于高斯分布的图像模型在水印嵌入和检测中存在的性能较差问题,提出一种基于奇异值分解和条件异方差的数字水印方法。给出一种运用小波变换和奇异值分解的加性水印嵌入方法。利用条件异方差模型较好地描述图像小波系数的"尖峰重尾"分布特性和异方差特性,来对小波系数进行建模。在此基础上,应用统计假设检验理论分析水印的盲检测过程,并推导水印检测中虚警概率和检测概率之间的工作特性关系。仿真结果表明了该方法的有效性,而且在诸如噪声、JPEG压缩、滤波、旋转以及缩放等攻击下具有较好的检测性能。
Image model based on Gauss distribution has low performance on watermark embedding and decoding. To solve this problem,a digital watermarking method is proposed based on singular value decomposition and conditional heteroscedasticity in the wavelet domain. By using wavelet transform and singular value decomposition,an additive watermarking embedding method was given in this paper. The conditional heteroscedasticity model was utilized to model the wavelet coefficients by taking advantages of the important characteristics such as "sharp peak and heavy tailed "marginal distribution and the heteroscedasticity. On the basis of this,the blind detection process of watermark was analyzed by using the theory of statistical hypothesis testing. And the relationship between false alarm probability and detection probability was derived. Simulation results prove the effectiveness of the additive watermark embedding method,and it has better detection performance under attacks such as noise,JPEG compression,filtering,rotation and scaling.
Image model based on Gauss distribution has low performance on watermark embedding and decoding. To solve this problem,a digital watermarking method is proposed based on singular value decomposition and conditional heteroscedasticity in the wavelet domain. By using wavelet transform and singular value decomposition,an additive watermarking embedding method was given in this paper. The conditional heteroscedasticity model was utilized to model the wavelet coefficients by taking advantages of the important characteristics such as "sharp peak and heavy tailed "marginal distribution and the heteroscedasticity. On the basis of this,the blind detection process of watermark was analyzed by using the theory of statistical hypothesis testing. And the relationship between false alarm probability and detection probability was derived. Simulation results prove the effectiveness of the additive watermark embedding method,and it has better detection performance under attacks such as noise,JPEG compression,filtering,rotation and scaling.
引文
[1]Urvoy M,Goudia D,Autrusseau F.Perceptual DFT watermarking with improved detection and robustness to geometrical distortions[J].IEEE Transactions on Information Forensics and Security,2014,9(7):1108-1119.
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[11]Sadreazami H,Ahmad M O,Swamy M N S.A Study of Multiplicative Watermark Detection in the Contourlet Domain Using Alpha-Stable Distributions[J].Image Processing IEEE Transactions on,2014,23(10):4348-4360.
[12]Akhaee M A,Sahraeian M E,Marvasti F.Contourlet-based image watermarking using optimum detector in a noisy environment.[J].IEEE Transactions on Image Processing APublication of the IEEE Signal Processing Society,2010,19(4):967-980.
[13]Rahman S M M,Ahmad M O,Swamy M N S.A New Statistical Detector for DWT-Based Additive Image Watermarking Using the Gauss-Hermite Expansion[J].IEEE Transactions on Image Processing,2009,18(8):1782-1796.
[14]Amirmazlaghani M.Additive watermark detection in the wavelet domain using 2D-GARCH model[J].Information Sciences,2016,370-371:1-17.
[15]Liu J,Moulin P.Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2001,10(11):1647-1658.
[16]Djukanovic S,Djurovi I.Detection optimization for the DCT-domain image watermarking system[C]//18th European Signal Processing Conference(EUSIPCO-2010).2010:23-27.
[2]Sendur L,Selesnick I W.Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency[J]IEEE Transactions on Image Processing,2002,50(11):2744-2756.
[3]Eslahi N,Aghagolzadeh A.Compressive sensing image restoration using adaptive curvelet thresholding and nonlocal sparse regularization[J].IEEE Transactions on Image Processing,2016,25(7):3126-3140.
[4]Hernández J R,Amado M,Pérezgonzález F.DCT-domain watermarking techniques for still images:detector performance analysis and a new structure[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2000,9(1):55-68.
[5]Portilla J,Strela V,Wainwright M J,et al.Image denoising using scale mixtures of Gaussians in the wavelet domain[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2003,12(11):1338-1351.
[6]Bian Y,Liang S.Image watermark detection in the wavelet domain using Bessel K densities[J].IET image processing,2013,7(4):281-289.
[7]Cheng Q,Huang T S.An additive approach to transform-domain information hiding and optimum detection structure[J].IEEE Transactions on Multimedia,2001,3(3):273-284.
[8]Ng T M,Grag H K.Maximum-likelihood detection in DWTdomain image watermarking using Laplacian modeling[J].IEEE Signal Processing Letters,2005,12(4):285-288.
[9]Tsai H H,Liu C C.Wavelet-based image watermarking with visibility range estimation based on HVS and neural networks[J].Pattern Recognition,2011,44(4):751-763.
[10]Kwitt R,Meerwald P,Uhl A.Lightweight detection of additive watermarking in the DWT-domain[J].IEEE Transactions on Image Processing,2011,20(2):474-484.
[11]Sadreazami H,Ahmad M O,Swamy M N S.A Study of Multiplicative Watermark Detection in the Contourlet Domain Using Alpha-Stable Distributions[J].Image Processing IEEE Transactions on,2014,23(10):4348-4360.
[12]Akhaee M A,Sahraeian M E,Marvasti F.Contourlet-based image watermarking using optimum detector in a noisy environment.[J].IEEE Transactions on Image Processing APublication of the IEEE Signal Processing Society,2010,19(4):967-980.
[13]Rahman S M M,Ahmad M O,Swamy M N S.A New Statistical Detector for DWT-Based Additive Image Watermarking Using the Gauss-Hermite Expansion[J].IEEE Transactions on Image Processing,2009,18(8):1782-1796.
[14]Amirmazlaghani M.Additive watermark detection in the wavelet domain using 2D-GARCH model[J].Information Sciences,2016,370-371:1-17.
[15]Liu J,Moulin P.Information-theoretic analysis of interscale and intrascale dependencies between image wavelet coefficients[J].IEEE Transactions on Image Processing A Publication of the IEEE Signal Processing Society,2001,10(11):1647-1658.
[16]Djukanovic S,Djurovi I.Detection optimization for the DCT-domain image watermarking system[C]//18th European Signal Processing Conference(EUSIPCO-2010).2010:23-27.