To fundamentally resist the steganalysis, coverless information hiding has been proposed, and it has become a research hotspot in the field of covert communication. However, the current methods not only require a huge image database, but also have a very low hidden capacity, making it difficult to apply practically. In order to solve the above problems, we propose a coverless information hiding method based on the generation of anime characters, which first converts the secret information into an attribute label set of the anime characters, and then uses the label set as a driver to directly generate anime characters by using the generative adversarial networks (GANs). The experimental results show that compared with the current methods, the hidden capacity of the proposed method is improved by nearly 60 times, and it also has good performance in image quality and robustness.

中文翻译：

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基于动漫角色生成的无遮挡信息隐藏

为了从根本上抵制隐写分析，已经提出了隐蔽的信息隐藏技术，它已成为隐蔽通信领域的研究热点。但是，当前的方法不仅需要庞大的图像数据库，而且隐藏容量非常低，难以实际应用。为了解决上述问题，我们提出了一种基于动漫角色生成的无遮挡信息隐藏方法，该方法首先将秘密信息转换为动漫角色的属性标签集，然后使用该标签集作为驱动程序直接通过使用生成对抗网络（GAN）生成动漫角色。实验结果表明，与现有方法相比，该方法的隐蔽能力提高了近60倍，