The distortion in steganography that usually comes from the modification or recoding on the cover image during the embedding process leaves the steganalyzer with possibility of discriminating. Faced with such a risk, we propose generative steganography with Kerckhoffs' principle (GSK) in this letter. In GSK, the secret messages are generated by a cover image using a generator rather than embedded into the cover, thus resulting in no modifications in the cover. To ensure the security, the generators are trained to meet Kerckhoffs' principle based on generative adversarial networks (GAN). Everything about the GSK system, except the extraction key, is public knowledge for the receivers. The secret messages can be outputted by the generator if and only if the extraction key and the cover image are both inputted. In the generator training procedures, there are two GANs, Message- GAN and Cover-GAN, designed to work jointly making the generated results under the control of the extraction key and the cover image. We provide experimental results on the training process and give an example of the working process by adopting a generator trained on MNIST, which demonstrate that GSK can use a cover image without any modification to generate messages, and without the extraction key or the cover image, only meaningless results would be obtained.
翻译:嵌入过程中对封面图像进行修改或重新编码,通常会造成对封面图像的扭曲,使封面图像进行修改或重新编码,使屏幕分析器有可能出现差别。 面对这种风险,我们提议用Kerckhoffs 原则( GSK) 在本文中提议使用Kerckhoffs 原则( GSK) 进行基因成形。 在 GGSK 中, 秘密信息是由使用发电机的封面图像生成的, 而不是嵌入封面, 从而没有修改封面。 为确保安全, 发电机经过培训, 以满足Kerckhoffes基于基因对抗网络( GAN) 的原则。 除提取键外, 有关 GSK 系统的一切内容都是接收器的公开知识。 只有在提取键和封面图像同时被输入的情况下, 才能由生成器输出秘密信息。 在发电机培训程序中, 有两个 GAN- 、 Mess- GAN 和 Cover-GAN, 旨在共同在提取钥匙和封面图像的控制下取得结果。 我们提供培训过程的实验结果, 并举一个工作过程的例子, 办法是在不使用经训练后对 MMSISIST 或图像进行无意义的复制。