HidingGAN: High Capacity Information Hiding with Generative Adversarial Network

• Published in: Computer graphics forum Vol. 38; no. 7; pp. 393 - 401
• Main Authors: Wang, Zihan, Gao, Neng, Wang, Xin, Xiang, Ji, Zha, Daren, Li, Linghui
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.10.2019
• Subjects: Algorithms; Artificial neural networks; CCS Concepts; Computing methodologies → Computer vision tasks; Generative adversarial networks; Machine learning; Pixels; Security and privacy → Privacy protections; Steganography
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/cgf.13846

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 × 256 pixels, improving over the previous best result of 0.4 bpp at 32 × 32 pixels.