基于几何校正与非下采样Shearlet变换的图像水印算法
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摘要
为了解决当前图像水印技术难以抵御几何失真,使其鲁棒性较低与误检率较高的问题,提出了几何校正与非下采样Shearlet变换的图像水印算法.首先,引入Cat映射,对水印信息图像进行置乱;随后,借助非下采样Shearlet变换机制,对载体图像进行处理,获取低通子带和高通子带,并将低通子带分割为尺寸相同的小块;通过修改低通子带的Shearlet系数,建立水印嵌入机制,将水印信息植入到载体图像中,获取水印密文;构建几何失真图像训练样本,基于极谐变换,计算水印图像的极谐变换系数模,充分描述其鲁棒特征;基于模糊支持向量机,预测几何失真参数,对水印图像进行几何校正;最后,再次利用非下采样Shearlet变换处理校正水印图像,获取低通子带小块,设计水印提取方法,复原其水印信息.实验结果显示:与当前图像水印算法相比,所提算法具有更高的不可感知能力与鲁棒性,对于各种几何攻击,所提取技术的复原水印与初始水印的相关系数均要高于0.95.
To solve the defects in current image watermarking technology,including poor robustness and the high falsedetection rate induced by the difficulty of resisting geometric distortion,we propose an image watermarking algorithm based on geometric correction and the non-subsample Shearlet transform. First,we use Cat mapping to permute the watermark information image. Then,we use a non-subsampled Shearlet transform to embed the watermarking information in the permutated image,output the low-pass subband and a series of high-pass subbands,and segment the low-pass subband into small blocks of the same size. To embed the information into a carrier image,we construct a watermark-embedding mechanism by modifying the non-subsampling Shearlet transform coefficients of the low-pass subband. Then,to fully describe the robust features,we construct geometric-distortion image-training samples and calculate the modulus of the polar harmonic transform coefficients of the watermark image based on the polar harmonic transformation. Then,we introduce a fuzzy support vector machine to predict the geometric-distortion parameter for correcting the watermark image. Lastly,we process the corrected watermark image using a non-sub-sampled Shearlet transform to obtain the low-pass subband,and design a watermark detection method for restoring the watermark information. The experimental results show that this algorithm has higher perceptual ability and better robustness than existing image watermarking algorithms,and for all kinds of geometric attacks,the correlation coefficients between the recovered and original watermarks are higher than 0.95.
To solve the defects in current image watermarking technology,including poor robustness and the high falsedetection rate induced by the difficulty of resisting geometric distortion,we propose an image watermarking algorithm based on geometric correction and the non-subsample Shearlet transform. First,we use Cat mapping to permute the watermark information image. Then,we use a non-subsampled Shearlet transform to embed the watermarking information in the permutated image,output the low-pass subband and a series of high-pass subbands,and segment the low-pass subband into small blocks of the same size. To embed the information into a carrier image,we construct a watermark-embedding mechanism by modifying the non-subsampling Shearlet transform coefficients of the low-pass subband. Then,to fully describe the robust features,we construct geometric-distortion image-training samples and calculate the modulus of the polar harmonic transform coefficients of the watermark image based on the polar harmonic transformation. Then,we introduce a fuzzy support vector machine to predict the geometric-distortion parameter for correcting the watermark image. Lastly,we process the corrected watermark image using a non-sub-sampled Shearlet transform to obtain the low-pass subband,and design a watermark detection method for restoring the watermark information. The experimental results show that this algorithm has higher perceptual ability and better robustness than existing image watermarking algorithms,and for all kinds of geometric attacks,the correlation coefficients between the recovered and original watermarks are higher than 0.95.
引文
[1] Zhang Z W,Wu L F,Xiao S Z. Adaptive reversible image watermarking algorithm based on IWT and level set[J]. EURASIP Journal on Ad-vances in Signal Processing,2017,22(1):101-116.
[2]邓春红,左开中,潘涛.一种RFID标签所有权完全转移协议[J].信息与控制,2016,45(6):677-683.Deng C H,Zuo K Z,Pan T. A complete transfer protocol for RFID tag ownership[J]. Information and Control,2016,45(6):677-683.
[3]邵东.优美图随机嵌入路径耦合像素优化的图像隐写[J].包装工程,2014,11(21):104-112.Shao D. Graceful graph,random embedding,path coupling,pixel optimization,image steganography[J]. Packaging Engineering,2014,11(21):104-112.
[4]陈琳,王建鹏.最优像素调整耦合基因算法的高容量图像隐写研究[J].计算机应用研究,2015,32(8):2429-2432.Chen L,Wang J P. Study on high capacity image steganography with optimal pixel adjusted coupling genetic algorithm[J]. Computer applica-tion research,2015,32(8):2429-2432.
[5] Zhang Z W,Wu L F,Yan Y Y. An improved reversible image watermarking algorithm based on difference expansion[J]. International Journalof Distributed Sensor Networks,2017,13(1):1015-1021.
[6] Tian H,Zhao Y,Ni R. LDFT-based watermarking resilient to local resynchronization attacks[J]. IEEE Transactions on Cybernetics,2014,43(6):2181-2190.
[7]陈青,王飞.基于SIFT的NSCT域抗几何攻击水印算法[J].包装工程,2017,38(5):178-182.Chen Qing,Wang F. Watermarking algorithm against geometric attacks in NSCT domain based on SIFT[J]. Packaging Engineering,2017,38(5):178-182.
[8] Fazli S,Moeini M. A robust image watermarking method based on DWT,DCT,and SVD using a new technique for correction of main geomet-ric attacks[J]. Optik-International Journal for Light and Electron Optics,2016,127(2):964-972.
[9]赵杰,王配配.基于NSST变换域WNNM和KAD算法的SAR图像去噪[J].郑州大学学报(自然科学版),2017,49(2):73-78.Zhao J,Wang P P. SAR image denoising based on NSST transform domain WNNM and KAD algorithm[J]. Journal of Zhengzhou University(Natural Science Edition),2017,49(2):73-78.
[10]陶飞翔,吴一全.非下采样Shearlet变换与参数化对数图像处理相结合的遥感图像增强[J].测绘学报,2015,44(8):884-892.Tao F X,Wu Y Q. Remote sensing image enhancement based on non sampled Shearlet transform and parameterized logarithmic image process-ing[J]. Journal of Surveying and mapping,2015,44(8):884-892.
[11]Tong Y,Chen J. Non-linear adaptive image enhancement in wireless sensor networks based on non-subsampled shearlet transform[J]. EUR-ASIP Journal on Wireless Communications and Networking,2017,18(1):109-117.
[12]Tsougenis E D,Papakostas G A. Towards adaptively of image watermarking in polar harmonic transforms domain[J]. Optics and Laser Tech-nology,2013,54(7):84-89.
[13]Amayeh G,Erol A,Bebis G,et al. Accurate and efficient computation of high order Zernike moments[M]//Lecture Notes in Computer Sci-ence. Berlin,Germany:Springer-Verlag,2005:462-469.
[14]孙倩,胡苏.基于改进cat映射与混沌系统的彩色图像快速加密算法[J].计算机应用研究,2017,34(1):233-237.Sun Q,Hu S. Fast color image encryption algorithm based on improved cat mapping and chaotic system[J]. Computer Application Research,2017,34(1):233-237.
[15]Chen L J,Shen A D. A novel public key image cryptosystem based on elliptic curve and arnold cat map[J]. Advanced Materials Research,2014,38(2):989-994.
[16]Batuwita R,Palade V. FSVM-CIL:Fuzzy support vector machines for class imbalance learning[J]. IEEE Transactions on Fuzzy Systems,2010,18(3):558-571.
[17]谢永芳,郭德益,朱红求.基于特征加权FSVM的锌液净化除铜工况评估[J].信息与控制,2014,43(3):350-354.Xie Y F,Guo D Y,Zhu H Q. Evaluation of removal of copper in liquid zinc purification based on feature weighted FSVM[J]. Information andControl,2014,43(3):350-354.
[18]Hong X K,Guo D L,Guo B P. Fuzzy support vector machine for Pol SAR image classification[J]. Advanced Materials Research,2013,640(1):1162-1167.
[19]Birajdar G K,Mankar V H. Blind method for rescaling detection and rescale factor estimation in digital images using periodic properties of in-terpolation[J]. AEU-International Journal of Electronics and Communications,2014,68(7):644-652.
[20]Zhu W,Drewes J,Gegenfurtner K R. Animal detection in natural images:Effects of color and image database[J]. Plos One,2013,8(10):78516-78522.
[2]邓春红,左开中,潘涛.一种RFID标签所有权完全转移协议[J].信息与控制,2016,45(6):677-683.Deng C H,Zuo K Z,Pan T. A complete transfer protocol for RFID tag ownership[J]. Information and Control,2016,45(6):677-683.
[3]邵东.优美图随机嵌入路径耦合像素优化的图像隐写[J].包装工程,2014,11(21):104-112.Shao D. Graceful graph,random embedding,path coupling,pixel optimization,image steganography[J]. Packaging Engineering,2014,11(21):104-112.
[4]陈琳,王建鹏.最优像素调整耦合基因算法的高容量图像隐写研究[J].计算机应用研究,2015,32(8):2429-2432.Chen L,Wang J P. Study on high capacity image steganography with optimal pixel adjusted coupling genetic algorithm[J]. Computer applica-tion research,2015,32(8):2429-2432.
[5] Zhang Z W,Wu L F,Yan Y Y. An improved reversible image watermarking algorithm based on difference expansion[J]. International Journalof Distributed Sensor Networks,2017,13(1):1015-1021.
[6] Tian H,Zhao Y,Ni R. LDFT-based watermarking resilient to local resynchronization attacks[J]. IEEE Transactions on Cybernetics,2014,43(6):2181-2190.
[7]陈青,王飞.基于SIFT的NSCT域抗几何攻击水印算法[J].包装工程,2017,38(5):178-182.Chen Qing,Wang F. Watermarking algorithm against geometric attacks in NSCT domain based on SIFT[J]. Packaging Engineering,2017,38(5):178-182.
[8] Fazli S,Moeini M. A robust image watermarking method based on DWT,DCT,and SVD using a new technique for correction of main geomet-ric attacks[J]. Optik-International Journal for Light and Electron Optics,2016,127(2):964-972.
[9]赵杰,王配配.基于NSST变换域WNNM和KAD算法的SAR图像去噪[J].郑州大学学报(自然科学版),2017,49(2):73-78.Zhao J,Wang P P. SAR image denoising based on NSST transform domain WNNM and KAD algorithm[J]. Journal of Zhengzhou University(Natural Science Edition),2017,49(2):73-78.
[10]陶飞翔,吴一全.非下采样Shearlet变换与参数化对数图像处理相结合的遥感图像增强[J].测绘学报,2015,44(8):884-892.Tao F X,Wu Y Q. Remote sensing image enhancement based on non sampled Shearlet transform and parameterized logarithmic image process-ing[J]. Journal of Surveying and mapping,2015,44(8):884-892.
[11]Tong Y,Chen J. Non-linear adaptive image enhancement in wireless sensor networks based on non-subsampled shearlet transform[J]. EUR-ASIP Journal on Wireless Communications and Networking,2017,18(1):109-117.
[12]Tsougenis E D,Papakostas G A. Towards adaptively of image watermarking in polar harmonic transforms domain[J]. Optics and Laser Tech-nology,2013,54(7):84-89.
[13]Amayeh G,Erol A,Bebis G,et al. Accurate and efficient computation of high order Zernike moments[M]//Lecture Notes in Computer Sci-ence. Berlin,Germany:Springer-Verlag,2005:462-469.
[14]孙倩,胡苏.基于改进cat映射与混沌系统的彩色图像快速加密算法[J].计算机应用研究,2017,34(1):233-237.Sun Q,Hu S. Fast color image encryption algorithm based on improved cat mapping and chaotic system[J]. Computer Application Research,2017,34(1):233-237.
[15]Chen L J,Shen A D. A novel public key image cryptosystem based on elliptic curve and arnold cat map[J]. Advanced Materials Research,2014,38(2):989-994.
[16]Batuwita R,Palade V. FSVM-CIL:Fuzzy support vector machines for class imbalance learning[J]. IEEE Transactions on Fuzzy Systems,2010,18(3):558-571.
[17]谢永芳,郭德益,朱红求.基于特征加权FSVM的锌液净化除铜工况评估[J].信息与控制,2014,43(3):350-354.Xie Y F,Guo D Y,Zhu H Q. Evaluation of removal of copper in liquid zinc purification based on feature weighted FSVM[J]. Information andControl,2014,43(3):350-354.
[18]Hong X K,Guo D L,Guo B P. Fuzzy support vector machine for Pol SAR image classification[J]. Advanced Materials Research,2013,640(1):1162-1167.
[19]Birajdar G K,Mankar V H. Blind method for rescaling detection and rescale factor estimation in digital images using periodic properties of in-terpolation[J]. AEU-International Journal of Electronics and Communications,2014,68(7):644-652.
[20]Zhu W,Drewes J,Gegenfurtner K R. Animal detection in natural images:Effects of color and image database[J]. Plos One,2013,8(10):78516-78522.