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
- 网络首发：2020-01，
  
  纸质出版：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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