  A great challenge to steganography has arisen with the wide application of
steganalysis methods based on convolutional neural networks (CNNs). To this
end, embedding cost learning frameworks based on generative adversarial
networks (GANs) have been proposed and achieved success for spatial
steganography. However, the application of GAN to JPEG steganography is still
in the prototype stage; its anti-detectability and training efficiency should
be improved. In conventional steganography, research has shown that the
side-information calculated from the precover can be used to enhance security.
However, it is hard to calculate the side-information without the spatial
domain image. In this work, an embedding cost learning framework for JPEG
Steganography via a Generative Adversarial Network (JS-GAN) has been proposed,
the learned embedding cost can be further adjusted asymmetrically according to
the estimated side-information. Experimental results have demonstrated that the
proposed method can automatically learn a content-adaptive embedding cost
function, and use the estimated side-information properly can effectively
improve the security performance. For example, under the attack of a classic
steganalyzer GFR with quality factor 75 and 0.4 bpnzAC, the proposed JS-GAN can
increase the detection error 2.58% over J-UNIWARD, and the estimated
side-information aided version JS-GAN(ESI) can further increase the security
performance by 11.25% over JS-GAN.
