Generative feedback residual network for high-capacity image hiding

被引:3
|
作者
Wu, Jianhua [1 ]
Lai, Zhengliang [1 ]
Zhu, Xishun [2 ]
机构
[1] Nanchang Univ, Sch Informat Engn, Nanchang, Jiangxi, Peoples R China
[2] Nanchang Univ, Sch Mechatron Engn, Nanchang 330031, Jiangxi, Peoples R China
基金
中国国家自然科学基金;
关键词
Image hiding; generative image hiding; convolutional neural network; feedback residual; STEGANALYSIS;
D O I
10.1080/09500340.2022.2093415
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
At present, there is always a potential threat in the process of information transmission. As a way to protect data security, image hiding has attracted extensive attention. Current image hiding algorithms have insufficient resistance to deep leaning based steganalysis algorithms and relatively low hiding capacity. This paper presents an image hiding algorithm based on a generative feedback residual network (GFR-Net), which hides multiple color secret images in a single color carrier image. First, several secret images and a carrier image were fed into the image hiding network, in which the secret images were embedded into the carrier image, resulting in an output of container image. A recovery network also based on GFR-Net was designed to reconstruct the secret images from the container. The extensive experiments for hiding normal and encrypted images show that the proposed image hiding model has a good performance in terms of payload and security.
引用
收藏
页码:870 / 886
页数:17
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