基于生成对抗网络的图像隐藏方案
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摘要
针对信息隐藏中修改载体嵌入信息的方法会留有痕迹,从根本上难以抵抗统计分析算法检测的问题,文章提出一种基于生成对抗网络的图像隐写方案,通过生成更合适的载体图像来提高信息隐藏的安全性,能够有效抵抗隐写分析算法的检测。该方案提出的策略是联合生成对抗网络和传统信息隐藏技术,将生成式载密图像和传统式载密图像作为输入,进行迭代优化,使生成器生成的图像更适合作为载体信息。与SGAN和SSGAN方案相比,该方案使得攻击者在隐写分析错误率上分别提高了9%和4.1%。实验结果表明,该方案在抗隐写分析和安全性指标上明显优于对比方案。
Focused on the issue that information-hidden carriers will leave traces of modification, and it is fundamentally difficult to resist the statistical steganalysis algorithm detection. A new security steganography model based on Generative Adversarial Networks(GAN) is proposed.By generating more appropriate carrier information to ensure the security of information hiding and improving the imperceptibility, we can effectively resist the steganalysis algorithm detection.The proposed strategy is to jointly generate the anti-network and traditional information hiding, and use the "generated" image and the "traditional"image as the discriminator input, and iteratively optimizes it,which make the image generated by the generator more suitable as carrier information. Compared with SGAN and SSGAN,this model reduces the detection accuracy of steganalysis by 9% and 4.1% respectively.Experimental results show that the new scheme is superior to the contrast algorithm in Antisteganography analysis and security indicators.
Focused on the issue that information-hidden carriers will leave traces of modification, and it is fundamentally difficult to resist the statistical steganalysis algorithm detection. A new security steganography model based on Generative Adversarial Networks(GAN) is proposed.By generating more appropriate carrier information to ensure the security of information hiding and improving the imperceptibility, we can effectively resist the steganalysis algorithm detection.The proposed strategy is to jointly generate the anti-network and traditional information hiding, and use the "generated" image and the "traditional"image as the discriminator input, and iteratively optimizes it,which make the image generated by the generator more suitable as carrier information. Compared with SGAN and SSGAN,this model reduces the detection accuracy of steganalysis by 9% and 4.1% respectively.Experimental results show that the new scheme is superior to the contrast algorithm in Antisteganography analysis and security indicators.
引文
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[2]TIEKEL A Z,RANKIN G A,SCHYNDEL R V.Electronic Watermark[C]//IEEE.Digital Image Computing,Technology and Applications,December 10-14,1993,Sydney,Australia.New York:IEEE,1993:666-673.
[3]YANG C H,WENG C Y,WANG S J.Adaptive Data Hiding in Edge Areas of Images with Spatial LSB Domain Systems[J].IEEETransactions on Information Forensics&Security,2008,3(3):488-497.
[4]GAO Hongtao,LU Wei,YANG Yuwang.Image Saliency Region Extraction Method Based on Adaptive Adjustment Kernel Function[J].Netinfo Security,2018,18(2):54-60.高洪涛,陆伟,杨余旺.基于自适应调节核函数的图像显著区域提取方法[J].信息网络安全,2018,18(2):54-60.
[5]PEVNY T,FILLER T,BAS P.Using High-Dimensional Image Models to Perform Highly Undetectable Steganography[EB/OL].https://tel.archives-ouvertes.fr/LAGIS/hal-00541353,2017-8-15.
[6]HOLUB V,FRIDRICH J.Designing Steganographic Distortion Using Directional Filters[C]//IEEE.International Workshop on Information Forensics and Security,December1-5,2013,Chicago,USA.New York:IEEE,2013:234-239.
[7]RUANAIDH J K O,Dowling W J,Boland F M.Phase Watermarking of Digital Images[C]//IEEE.2013 International Conference on Image Processing,May 11-14,2013,San Francisco,USA.New York:IEEE,2013:1673-1687.
[8]COX I J,KILIAN J,LEIGHTON F T.Secure Spread Spectrum Watermarking for Multimedia[J].IEEE Transactions on Image Processing,2010,6(12):1673-1687.
[9]LIN W H,HORNY S J,KAO T W.An Efficient Watermarking Method Based on Significant Difference of Wavelet Coefficient Quantization[J].IEEE Transactions on Multimedia,2008,10(5):746-757.
[10]DENIS V,BORIS B,BUENAIV E.Generative Adversarial Networks for Image Steganography[R].ICLR 2016 Open Review,2016.
[11]SHI H,DONG J,WANG W,et al.SSGAN:Secure Steganography Based on Generative Adversarial Networks[EB/OL].https://www.researchgate.net/publication/318258864_SSGAN_Secure_Steganography_Based_on_Generative_Adversarial_Networks,2017-7-30.
[12]GOODFELLOW I,POUGAT A J,MIRZA M.Generative Adversarial Networks[EB/OL].http://arxiv.org/abs/1406.2661,2014-6-10.
[13]WANG Kunfeng,YU Chao,DUAN Yanjie,et al.Research Progress and Prospects of Generative Confrontation Network GAN[J].Acta Automatica Sinica,2017,43(3):321-332.王坤峰,苟超,段艳杰,等.生成式对抗网络GAN的研究进展与展望[J].自动化学报,2017,43(3):321-332.
[14]RADFORD A,METZ L,CHINTALA S.Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[J].Computer Science,2015,1(11):132-141.
[15]MIRZA M,OSINDRO S.Conditional Generative Adversarial Nets[EB/OL].https://www.researchgate.net/publication/268079622_Conditional_Generative_Adversarial_Nets,2014-11-30.
[16]CHEN X,DUAN Y,HOUTHOOFT R,et al.InfoGAN:Interpretable Representation Learning by Information MaximiZing Generative Adversarial Nets[EB/OL].https://www.researchgate.net/publication/305166882_InfoGAN_Interpretable_Representation_Learning_by_Information_Maximizing_Generative_Adversarial_Nets,2016-6-30.
[17]ARJOVSKY M,BOTTOU L.Towards Principled Methods for Training Generative Adversarial Networks[EB/OL].http://arxiv.org/abs/1701.04862,2017-2-9.
[18]CUI S,JIANG Y.Effective Lipschitz Constraint Enforcement for Wasserstein GAN Training[C]//IEEE.2017 2nd IEEEInternational Conference on Computational Intelligence and Applications(ICCIA)IEEE,September 8-11,2017,Beijing,China.New York:IEEE,2017:74-78.
[19]QIAN Y,DONG J,WANG W,et al.Deep Learning for Steganalysis via Convolutional Neural Networks[EB/OL].https://www.researchgate.net/publication/282265577_Deep_learning_for_steganalysis_via_convolutional_neural_networks,2015-4-30.
[20]ACHAB M,GUILLOUX A,S G,et al.SGD with Variance Reduction beyond Empirical Risk Minimization[EB/OL].https://www.researchgate.net/publication/283043436_SGD_with_Variance_Reduction_beyond_Empirical_Risk_Minimization,2015-10-30.
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