收稿日期: 2018-01-28
网络出版日期: 2018-03-31
基金资助
国家自然科学基金（No.61379152，No.61403417）资助
Coverless Information Hiding Based on Generative Adversarial Networks
Received date: 2018-01-28
Online published: 2018-03-31
传统信息隐藏算法通过修改载体来嵌入秘密信息，难以从根本上抵抗基于统计的信息隐藏分析方法的检测，为此提出一种基于生成对抗网络的无载体信息隐藏方法.该方法将生成对抗网络中的类别标签替换为秘密信息作为驱动，直接生成含密图像进行传递，再通过判别器将含密图像中的秘密信息提取出来，并借助生成对抗网络实现无载体信息隐藏.实验结果和分析表明，该隐藏方法在隐写容量、抗隐写分析、安全性方面均有良好表现.
刘明明, 张敏情, 刘佳, 高培贤, 张英男 . 基于生成对抗网络的无载体信息隐藏[J]. 应用科学学报, 2018 , 36(2) : 371 -382 . DOI: 10.3969/j.issn.0255-8297.2018.02.015
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.
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