HidingGAN: High Capacity Information Hiding with Generative Adversarial Network

doi:10.1111/cgf.13846

	HidingGAN: High Capacity Information Hiding with Generative Adversarial Network
	Wang, Zihan 1,2,3; Gao, Neng 1,3; Wang, Xin 3; Xiang, Ji 3; Zha, Daren 3; Li, Linghui 4
	2019-10-01
发表期刊	COMPUTER GRAPHICS FORUM
ISSN	0167-7055
卷号	38 期号:7 页码:393-401
摘要	Image steganography is the technique of hiding secret information within images. It is an important research direction in the security field. Benefitting from the rapid development of deep neural networks, many steganographic algorithms based on deep learning have been proposed. However, two problems remain to be solved in which the most existing methods are limited by small image size and information capacity. In this paper, to address these problems, we propose a high capacity image steganographic model named HidingGAN. The proposed model utilizes a new secret information preprocessing method and Inception-ResNet block to promote better integration of secret information and image features. Meanwhile, we introduce generative adversarial networks and perceptual loss to maintain the same statistical characteristics of cover images and stego images in the high-dimensional feature space, thereby improving the undetectability. Through these manners, our model reaches higher imperceptibility, security, and capacity. Experiment results show that our HidingGAN achieves the capacity of 4 bits-per-pixel (bpp) at 256 x 256 pixels, improving over the previous best result of 0.4 bpp at 32 x 32 pixels.
DOI	10.1111/cgf.13846
收录类别	SCI
语种	英语
资助项目	National Key Research and Development Program of China ; National Natural Science Foundation of China[U163620068]
WOS研究方向	Computer Science
WOS类目	Computer Science, Software Engineering
WOS记录号	WOS:000496351100036
出版者	WILEY
引用统计	被引频次：15[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/14979
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Xin
作者单位	1.Chinese Acad Sci, State Key Lab Informat Secur, Inst Informat Engn, Beijing, Peoples R China 2.Univ Chinese Acad Sci, Sch Cyber Secur, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Informat Engn, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Comp Technol, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Wang, Zihan,Gao, Neng,Wang, Xin,et al. HidingGAN: High Capacity Information Hiding with Generative Adversarial Network[J]. COMPUTER GRAPHICS FORUM,2019,38(7):393-401.
APA	Wang, Zihan,Gao, Neng,Wang, Xin,Xiang, Ji,Zha, Daren,&Li, Linghui.(2019).HidingGAN: High Capacity Information Hiding with Generative Adversarial Network.COMPUTER GRAPHICS FORUM,38(7),393-401.
MLA	Wang, Zihan,et al."HidingGAN: High Capacity Information Hiding with Generative Adversarial Network".COMPUTER GRAPHICS FORUM 38.7(2019):393-401.