摘要
针对现有信息隐藏算法存在隐写容量低、信息提取困难以及安全性差等问题,本文提出了一种基于生成对抗网络的高容量信息隐藏算法(High Capacity Information Hiding Al⁃gorithm Based on GAN,HCGAN).在秘密信息嵌入方面,使用基于Im-Residual结构的编码器将秘密信息嵌入载体图像中,避免了秘密信息嵌入时由卷积层提取特征导致的信息损失.在秘密信息提取方面,使用基于稠密结构的解码器从含秘图像中提取出秘密信息,利用特征复用来增加秘密信息的提取率.在抗隐写分析方面,利用基于隐写分析的鉴别器与基于Im Residual结构的编码器进行对抗训练,以提高含秘图像的抗隐写分析能力.实验表明,经过对抗训练后,HCGAN在2 bpp嵌入率下比WOW和S-UNIWARD在0.4 bpp嵌入率下具有更低的隐写分析检测率.
Aiming at the problems of low steganographic capacity,difficult information extraction,and poor secu⁃rity in existing information hiding algorithms,this paper proposes a high capacity information hiding algorithm based on GAN(HCGAN).For secret information embedding,an Im-Residual structure-based encoder is applied to embed the secret information into the carrier image,avoiding the information loss caused by the feature extraction of the con⁃volution layer.For secret information extraction,a dense structure-based decoder is utilized to extract secret informa⁃tion from the secret image,and feature reuse is used to increase the extraction rate of secret information.In terms of anti-steganalysis,the discriminator based on steganalysis and the encoder based on Im-Residual structure are used for adversarial training to improve the anti-steganalysis ability of the secret image.Experiments show that after adver⁃sarial training,HCGAN has a lower steganalysis detection rate at an embedding rate of 2bpp than the WOW and SUNIWARD algorithms at an embedding rate of 0.4bpp.
作者
张克君
李旭
于新颖
冯丽雯
秦昊聪
张健毅
ZHANG Kejun;LI Xu;YU Xinying†;FENG Liwen;QIN Haocong;ZHANG Jianyi(Department of Cyberspace Security,Beijing Electronic Science and Technology Institute,Beijing 100071,China;School of Cyberspace Security,Beijing University of Posts and Telecommunications,Beijing 100876,China)
出处
《湖南大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2022年第4期35-46,共12页
Journal of Hunan University:Natural Sciences
基金
北京高校高精尖学科建设项目(20210086Z0401)
国家重点研发计划网络空间安全重大专项课题资助(2018YFB0803601)。
关键词
信息隐藏
深度学习
生成对抗网络
自编码器
卷积神经网络
information hiding
deep learning
generative adversarial networks
autoencoder
convolutional neu⁃ral network
