DGANS：基于双重生成式对抗网络的稳健图像隐写模型

竺乐庆; 郭钰; 莫凌强; 张大兴

doi:10.11959/j.issn.1000-436x.2020019

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DGANS：基于双重生成式对抗网络的稳健图像隐写模型

学术论文 | 更新时间：2024-06-05

- DGANS：基于双重生成式对抗网络的稳健图像隐写模型
- DGANS:robustness image steganography model based on double GAN
- 通信学报 2020年41卷第1期页码：125-133
- 作者机构：
  
  1. 浙江工商大学计算机与信息工程学院，浙江杭州 310018
  2. 杭州电子科技大学计算机学院，浙江杭州 310018
- 作者简介：
  
  [ "竺乐庆（1972- ），女，浙江嵊州人，博士，浙江工商大学副教授、硕士生导师，主要研究方向为图像处理、模式识别、视频处理、信息隐藏等" ]
  [ "郭钰（1995- ），男，安徽宿州人，浙江工商大学硕士生，主要研究方向为图像处理、模式识别、图像信息隐藏等" ]
  [ "莫凌强（1994- ），男，浙江嘉兴人，浙江工商大学硕士生，主要研究方向为模式识别、图像处理、图像信息隐藏等" ]
  [ "张大兴（1971- ），男，浙江嵊州人，博士，杭州电子科技大学副教授、硕士生导师，主要研究方向为信息安全、多媒体技术、软件工程等" ]
- 基金信息：
  
  国家自然科学基金资助项目(61572160);浙江省自然科学基金资助项目(LY20F020002)
- DOI：10.11959/j.issn.1000-436x.2020019
  中图分类号： TP391
- 网络出版日期：2020-01，
  
  纸质出版日期：2020-01-25
- 稿件说明：
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竺乐庆, 郭钰, 莫凌强, 等. DGANS：基于双重生成式对抗网络的稳健图像隐写模型[J]. 通信学报, 2020,41(1):125-133.

Leqing ZHU, Yu GUO, Lingqiang MO, et al. DGANS:robustness image steganography model based on double GAN[J]. Journal on communications, 2020, 41(1): 125-133.
竺乐庆, 郭钰, 莫凌强, 等. DGANS：基于双重生成式对抗网络的稳健图像隐写模型[J]. 通信学报, 2020,41(1):125-133. DOI： 10.11959/j.issn.1000-436x.2020019.

Leqing ZHU, Yu GUO, Lingqiang MO, et al. DGANS:robustness image steganography model based on double GAN[J]. Journal on communications, 2020, 41(1): 125-133. DOI： 10.11959/j.issn.1000-436x.2020019.

摘要

深度卷积神经网络可有效地应用于大容量图像信息隐写，然而其稳健性研究却鲜有报道。双重生成式对抗网络（DGANS）模型对深度学习框架应用于图像隐写时，针对小幅度几何变换攻击进行了优化设计，从而提高模型的稳健性。DGANS由2个串联的生成式对抗网络构成，可将灰度图像隐藏到相同大小的彩色或灰度图像中并还原。通过对生成的含密图像进行数据增强并进一步强化训练提取网络，使提取网络对输入图像的几何变换具有适应性。实验结果表明，DGAN不仅可以实现高容量的图像信息隐写，而且可以对抗一定范围内的几何攻击，比同类模型有更好的稳健性。

Abstract

Deep convolutional neural networks can be effectively applied to large-capacity image steganography

but the research on their robustness is rarely reported.The DGANS (double-GAN-based steganography) applies the deep learning framework in image steganography

which is optimized to resist small geometric distortions so as to improve the model’s robustness.DGANS is made up of two consecutive generative adversarial networks that can hide a grayscale image into another color or grayscale image of the same size and can restore it later.The generated stego-images are augmented and used to further train and strengthen the reveal network so as to make it adaptive to small geometric distortion of input images.Experimental results suggest that DGANS can not only realize high-capacity image steganography

but also can resist geometric attacks within certain range

which demonstrates better robustness than similar models.

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references

沈昌祥 , 张焕国 , 冯登国 , 等 . 信息安全综述 [J ] . 中国科学(信息科学) , 2007 , 37 ( 1 ): 129 - 150 .

SHEN C X , ZHANG H G , FENG D G , et al . Survey on information security [J ] . Science in China Series (Information Sciences) , 2007 , 37 ( 1 ): 129 - 150 .

王向阳 , 杨红颖 . DCT域自适应彩色图像二维数字水印算法研究 [J ] . 计算机辅助设计与图形学学报 , 2004 , 16 ( 2 ): 243 - 247 .

WANG X Y , YANG H Y . Adaptive 2-D color image watermarking based on DCT [J ] . Journal of Computer-Aided Design and Computer Graphics , 2004 , 16 ( 2 ): 243 - 247 .

王向阳 , 杨红颖 . 基于视觉掩蔽特性的小波域彩色数字水印技术 [J ] . 计算机辅助设计与图形学学报 , 2004 , 16 ( 9 ): 1240 - 1243 .

WANG X Y , YANG H Y . Color digital watermarking based on integer lifting wavelet transform and visual masking [J ] . Journal of Computer-Aided Design and Computer Graphics , 2004 , 16 ( 9 ): 1240 - 1243 .

PEVNÝ T , FILLER T , BAS P . Using high~dimensional image models to perform highly undetectable steganography [C ] // International Workshop on Information Hiding . Springer , 2010 : 161 - 177 .

HOLUB V , FRIDRICH J , DENEMARK T . Universal distortion function for steganography in an arbitrary domain [J ] . EURASIP Journal on Information Security , 2014 , 1 ( 1 ):1.

HOLUB V , FRIDRICH J . Designing steganographic distortion using directional filters [C ] // IEEE International Workshop on Information Forensics ＆ Security . IEEE , 2012 : 234 - 239 .

SHI H C , DONG J , WANG W , et al . SSGAN:secure steganography based on generative adversarial networks [C ] // 18th Pacific-Rim Conference on Multimedia . Springer , 2017 : 534 - 544 .

ARJOVSKY M , CHINTALA S , BOTTOU L . Wasserstein GAN [J ] . arXiv Preprint,arXiv:1701.07875 , 2017 : 1 - 32 .

HAYES J , DANEZIS G . Ste-GAN-ography:generating steganographic images via adversarial training [J ] . arXiv Preprint,arXiv:170300371v2 , 2017 : 1 - 9 .

REHMAN A U , RAHIM R , NADEEM S , et al . End-to-end trained CNN encode-decoder networks for image steganography [J ] . arXiv Preprint,arXiv:1711.07201 , 2017 : 1 - 5 .

BALUJA S , . Hiding images in plain sight:deep steganography [C ] // Advances in Neural Information Processing Systems . 2017 :

CHU C , ZHMOGINOV A , SANDLER M . CycleGAN,a master of steganography [C ] // Thirty-first Conference on Neural Information Processing Systems . 2017 : 1 - 6 .

TANG W , TAN S , LI B , et al . Automatic steganographic distortion learning using a generative adversarial network [J ] . IEEE Signal Processing Letters , 2017 , 24 ( 10 ): 1547 - 1551 .

ZHANG R , DONG S Q , LIU J Y . Invisible steganography via generative adversarial networks [J ] . Multimedia Tools and Applications , 2019 , 78 ( 7 ): 8559 - 8575 .

WU P , YANG Y , LI X . StegNet:MEGA image steganography capacity with deep convolutional network [J ] . Future Internet , 2018 , 10 ( 6 ): 54 - 68 .

DUAN X T , JIA K , LI B X , et al . Reversible image steganography scheme based on a U-Net structure [J ] . IEEE Access , 2019 , 7 ( 1 ): 9314 - 9323 .

XU G , . Deep convolutional neural network to detect J-UNIWARD [C ] // The 5th ACM Workshop on Information Hiding and Multimedia Security . 2017 : 67 - 73 .

SZEGEDY C , VANHOUCKE V , IOFFE S , et al . Rethinking the inception architecture for computer vision [C ] // IEEE Conference on Computer Vision and Pattern Recognition . 2016 : 2818 - 2826 .

IOFFE S , SZEGEDY C . Batch normalization:accelerating deep network training by reducing internal covariate shift [J ] . arXiv Preprint,arXiv:1502 ,03167,2015.

GOODFELLOW I , POUGET-ABADIE J , MIRZA M , et al . Generative adversarial nets [C ] // International Conference on Neural Information Processing Systems . 2014 : 2672 - 2680 .

WANG Z , BOVIK A , SHEIKH H R , et al . Image quality assessment:from error visibility to structural similarity [J ] . IEEE Transactions on Image Processing , 2004 , 13 ( 4 ): 600 - 612 .

EVERINGHAM M . The PASCAL visual object classes challenge(VOC2007)Results [J ] . Lecture Notes in Computer Science , 2007 , 111 ( 1 ): 98 - 136 .

YE J , NI J , YI Y . Deep learning hierarchical representations for image steganalysis [J ] . IEEE Transactions on Information Forensics and Security , 2017 , 12 ( 11 ): 2545 - 2557 .

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