HidingGAN: High Capacity Information Hiding with Generative Adversarial Network

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 problem...

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Veröffentlicht in:Computer graphics forum Jg. 38; H. 7; S. 393 - 401
Hauptverfasser: Wang, Zihan, Gao, Neng, Wang, Xin, Xiang, Ji, Zha, Daren, Li, Linghui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.10.2019
Schlagworte:
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
Online-Zugang:Volltext
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Zusammenfassung: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.
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ISSN:0167-7055
1467-8659
DOI:10.1111/cgf.13846