  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.
