基于生成对抗网络的无载体信息隐藏

doi:10.3969/j.issn.0255-8297.2018.02.015

Abstract

Abstract:

Traditional image steganography algorithms, which embed the secret information by modifying the content of the image more or less, are hard to resist the detection of image steganalysis tools. To address this problem, a novel coverless information hiding method based on generative adversarial networks is proposed in this paper. The main idea of the method is that the class label of generative adversarial networks is replaced with the secret information as a driver to generate hidden image directly. And the secret information is extracted from the hidden image through a discriminator. Experimental results show that this hidden algorithm ensures good performs in terms of steganography capacity, anti-steganalysis and safety.

Key words: generative adversarial networks(GAN), information hiding, coverless information hiding, auxiliary classifer GAN (ACGAN)

CLC Number:

P751.1

LIU Ming-ming, ZHANG Min-qing, LIU Jia, GAO Pei-xian, ZHANG Ying-nan. Coverless Information Hiding Based on Generative Adversarial Networks[J]. Journal of Applied Sciences, 2018, 36(2): 371-382.

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Coverless Information Hiding Based on Generative Adversarial Networks

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