基于二元Weibull分布的非下采样Shearlet域图像水印算法
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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.
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.
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.
引文
[1]Shih F Y.Digital Watermarking and Steganography:Fundamentals and Techniques[M].2nd ed.Boca Raton,USA:CRC,2017
[2]Qasim A F,Meziane F,Aspin R.Digital watermarking:Applicability for developing trust in medical imaging workflows state of the art review[J].Computer Science Review,2018,27:45- 60
[3]Wang Chunpeng,Wang Xingyuan,Zhang Chuan,et al.Stereo image zero-watermarking algorithm based on ternary polar harmonic Fourier moments and chaotic mapping[J].Scientia Sinica Informationis,2018,48(1):79- 99 (in Chinese)(王春鹏,王兴元,张川,等.基于三元数极谐-Fourier矩和混沌映射的立体图像零水印算法[J].中国科学:信息科学,2018,48(1):79- 99)
[4]Panah A S,Schyndel R V,Sellis T.On the properties of non-media digital watermarking:A review of state of the art techniques[J].IEEE Access,2016,4:2670- 2704
[5]Wang Xiangyang,Yang Hongying,Niu Panpan,et al.Quaternion exponent moments based robust color image watermarking[J].Journal of Computer Research and Development,2016,53(3):651- 665 (in Chinese)(王向阳,杨红颖,牛盼盼,等.基于四元数指数矩的抗几何攻击彩色图像水印算法[J].计算机研究与发展,2016,53(3):651- 665)
[6]Shi Hui,Li Mingchu.Safe variable-capacity self-recovery watermarking scheme[J].Journal of Computer Research and Development,2014,51(8):1715- 1726 (in Chinese)(石慧,李明楚.安全的变容量恢复水印方案[J].计算机研究与发展,2014,51(8):1715- 1726)
[7]Barni M,Bartolini F,DeRosa A,et al.Optimum decoding and detection of multiplicative watermarks[J].IEEE Transactions on Signal Processing,2003,51(4):1118- 1123
[8]Do M N,Vetterli M.Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models[J].IEEE Transactions on Multimedia,2012,4(4):517- 527
[9]Do M N,Vetterli M.Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance[J].IEEE Transactions on Image Processing,2002,11(2):146- 158
[10]Wang Chuntao,Ni Jiangqun,Huang Jiwu.An informed watermarking scheme using hidden Markov model in the wavelet domain[J].IEEE Transactions on Information Forensics and Security,2012,7(3):853- 867
[11]Amini M,Ahmad M O,Swamy M N S.A new locally optimum watermark detection using vector-based hidden Markov model in wavelet domain[J].Signal Processing,2017,137:213- 222
[12]Akhaee M A,Sahraeian S M E,Marvasti F.Contourlet-based image watermarking using optimum detector in a noisy environment[J].IEEE Transactions on Image Processing,2010,19(4):967- 980
[13]Bian Yong,Liang S.Locally optimal detection of image watermarks in the wavelet domain using Bessel K form distribution[J].IEEE Transactions on Image Processing,2013,22(6):2372- 2384
[14]Etemad S,Amirmazlaghani M.A new multiplicative watermark detector in the contourlet domain using t location-scale distribution[J].Pattern Recognition,2018,77:99- 112
[15]Sadreazami H,Ahmad M O,Swamy M N S.Multiplicative watermark decoder in contourlet domain using the normal inverse Gaussian distribution[J].IEEE Transactions on Multimedia,2016,18(2):196- 207
[16]Wang Xiangyang,Liu Yunan,Xu Huan,et al.Blind optimum detector for robust image watermarking in nonsubsampled shearlet domain[J].Information Sciences,2016,372:634- 654
[17]Zhang Yue,Shu Huazhong,Wu Jiasong,et al.A robust watermarking algorithm based on a new orthogonal complex transform[J].Acta Electronica Sinica,2013,41(8):1574- 1579 (in Chinese)(张悦,舒华忠,伍家松,等.一种基于新的正交复数变换的鲁棒水印算法[J].电子学报,2013,41(8):1574- 1579)
[18]Dong Luan,Yan Qin,Lü Yong,et al.Full band watermarking in DCT domain with Weibull model[J].Multmedia Tools and Applications,2017,76(2):1983- 2000
[19]Sadreazami H,Ahmad M O,Swamy M N S.A study of multiplicative watermark detection in the contourlet domain using alpha-stable distributions[J].IEEE Transactions on Image Processing,2014,23(10):4348- 4360
[20]Sadreazami H,Ahmad M O,Swamy M N S.A robust multiplicative watermark detector for color images in sparse domain[J].IEEE Transactions on Circuits and System,2015,62(12):1159- 1163
[21]Easley G,Labate D,Lim W Q.Sparse directional image representations using the discrete shearlet transform[J].Applied and Computational Harmonic Analysis,2008,25(1):25- 46
[22]Lim W Q.A new directional transform and compactly supported shearlet frames[J].IEEE Transactions on Image Processing,2010,19(5):1166- 1180
[23]Fisher N I,Switzer P.Chi-plots for assessing dependence[J].Biometrica,1985,72(2):253- 265
[24]Villanueva D,Feijóo A,Pazos J L.Multivariate Weibull distribution for wind speed and wind power behavior assessment[J].Resources,2013,2(3):370- 384
[2]Qasim A F,Meziane F,Aspin R.Digital watermarking:Applicability for developing trust in medical imaging workflows state of the art review[J].Computer Science Review,2018,27:45- 60
[3]Wang Chunpeng,Wang Xingyuan,Zhang Chuan,et al.Stereo image zero-watermarking algorithm based on ternary polar harmonic Fourier moments and chaotic mapping[J].Scientia Sinica Informationis,2018,48(1):79- 99 (in Chinese)(王春鹏,王兴元,张川,等.基于三元数极谐-Fourier矩和混沌映射的立体图像零水印算法[J].中国科学:信息科学,2018,48(1):79- 99)
[4]Panah A S,Schyndel R V,Sellis T.On the properties of non-media digital watermarking:A review of state of the art techniques[J].IEEE Access,2016,4:2670- 2704
[5]Wang Xiangyang,Yang Hongying,Niu Panpan,et al.Quaternion exponent moments based robust color image watermarking[J].Journal of Computer Research and Development,2016,53(3):651- 665 (in Chinese)(王向阳,杨红颖,牛盼盼,等.基于四元数指数矩的抗几何攻击彩色图像水印算法[J].计算机研究与发展,2016,53(3):651- 665)
[6]Shi Hui,Li Mingchu.Safe variable-capacity self-recovery watermarking scheme[J].Journal of Computer Research and Development,2014,51(8):1715- 1726 (in Chinese)(石慧,李明楚.安全的变容量恢复水印方案[J].计算机研究与发展,2014,51(8):1715- 1726)
[7]Barni M,Bartolini F,DeRosa A,et al.Optimum decoding and detection of multiplicative watermarks[J].IEEE Transactions on Signal Processing,2003,51(4):1118- 1123
[8]Do M N,Vetterli M.Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models[J].IEEE Transactions on Multimedia,2012,4(4):517- 527
[9]Do M N,Vetterli M.Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance[J].IEEE Transactions on Image Processing,2002,11(2):146- 158
[10]Wang Chuntao,Ni Jiangqun,Huang Jiwu.An informed watermarking scheme using hidden Markov model in the wavelet domain[J].IEEE Transactions on Information Forensics and Security,2012,7(3):853- 867
[11]Amini M,Ahmad M O,Swamy M N S.A new locally optimum watermark detection using vector-based hidden Markov model in wavelet domain[J].Signal Processing,2017,137:213- 222
[12]Akhaee M A,Sahraeian S M E,Marvasti F.Contourlet-based image watermarking using optimum detector in a noisy environment[J].IEEE Transactions on Image Processing,2010,19(4):967- 980
[13]Bian Yong,Liang S.Locally optimal detection of image watermarks in the wavelet domain using Bessel K form distribution[J].IEEE Transactions on Image Processing,2013,22(6):2372- 2384
[14]Etemad S,Amirmazlaghani M.A new multiplicative watermark detector in the contourlet domain using t location-scale distribution[J].Pattern Recognition,2018,77:99- 112
[15]Sadreazami H,Ahmad M O,Swamy M N S.Multiplicative watermark decoder in contourlet domain using the normal inverse Gaussian distribution[J].IEEE Transactions on Multimedia,2016,18(2):196- 207
[16]Wang Xiangyang,Liu Yunan,Xu Huan,et al.Blind optimum detector for robust image watermarking in nonsubsampled shearlet domain[J].Information Sciences,2016,372:634- 654
[17]Zhang Yue,Shu Huazhong,Wu Jiasong,et al.A robust watermarking algorithm based on a new orthogonal complex transform[J].Acta Electronica Sinica,2013,41(8):1574- 1579 (in Chinese)(张悦,舒华忠,伍家松,等.一种基于新的正交复数变换的鲁棒水印算法[J].电子学报,2013,41(8):1574- 1579)
[18]Dong Luan,Yan Qin,Lü Yong,et al.Full band watermarking in DCT domain with Weibull model[J].Multmedia Tools and Applications,2017,76(2):1983- 2000
[19]Sadreazami H,Ahmad M O,Swamy M N S.A study of multiplicative watermark detection in the contourlet domain using alpha-stable distributions[J].IEEE Transactions on Image Processing,2014,23(10):4348- 4360
[20]Sadreazami H,Ahmad M O,Swamy M N S.A robust multiplicative watermark detector for color images in sparse domain[J].IEEE Transactions on Circuits and System,2015,62(12):1159- 1163
[21]Easley G,Labate D,Lim W Q.Sparse directional image representations using the discrete shearlet transform[J].Applied and Computational Harmonic Analysis,2008,25(1):25- 46
[22]Lim W Q.A new directional transform and compactly supported shearlet frames[J].IEEE Transactions on Image Processing,2010,19(5):1166- 1180
[23]Fisher N I,Switzer P.Chi-plots for assessing dependence[J].Biometrica,1985,72(2):253- 265
[24]Villanueva D,Feijóo A,Pazos J L.Multivariate Weibull distribution for wind speed and wind power behavior assessment[J].Resources,2013,2(3):370- 384