  Image steganography is the art and science of using images as cover for
covert communications. With the development of neural networks, traditional
image steganography is more likely to be detected by deep learning-based
steganalysis. To improve upon this, we propose image steganography network
based on style transfer, and the embedding of secret messages can be disguised
as image stylization. We embed secret information while transforming the
content image style. In latent space, the secret information is integrated into
the latent representation of the cover image to generate the stego images,
which are indistinguishable from normal stylized images. It is an end-to-end
unsupervised model without pre-training. Extensive experiments on the benchmark
dataset demonstrate the reliability, quality and security of stego images
generated by our steganographic network.
