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A great challenge to steganography has arisen with the wide application ofsteganalysis methods based on convolutional neural networks (CNNs). To thisend, embedding cost learning frameworks based on generative adversarialnetworks (GANs) have been proposed and achieved success for spatialsteganography. However, the application of GAN to JPEG steganography is stillin the prototype stage; its anti-detectability and training efficiency shouldbe improved. In conventional steganography, research has shown that theside-information calculated from the precover can be used to enhance security.However, it is hard to calculate the side-information without the spatialdomain image. In this work, an embedding cost learning framework for JPEGSteganography via a Generative Adversarial Network (JS-GAN) has been proposed,the learned embedding cost can be further adjusted asymmetrically according tothe estimated side-information. Experimental results have demonstrated that theproposed method can automatically learn a content-adaptive embedding costfunction, and use the estimated side-information properly can effectivelyimprove the security performance. For example, under the attack of a classicsteganalyzer GFR with quality factor 75 and 0.4 bpnzAC, the proposed JS-GAN canincrease the detection error 2.58% over J-UNIWARD, and the estimatedside-information aided version JS-GAN(ESI) can further increase the securityperformance by 11.25% over JS-GAN.

JPEG Steganography with Embedding Cost Learning and Side-Information Estimation