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Although significant progress in automatic learning of steganographic costhas been achieved recently, existing methods designed for spatial images arenot well applicable to JPEG images which are more common media in daily life.The difficulties of migration mostly lie in the unique and complicated JPEGcharacteristics caused by 8x8 DCT mode structure. To address the issue, in thispaper we extend an existing automatic cost learning scheme to JPEG, where theproposed scheme called JEC-RL (JPEG Embedding Cost with Reinforcement Learning)is explicitly designed to tailor the JPEG DCT structure. It works with theembedding action sampling mechanism under reinforcement learning, where apolicy network learns the optimal embedding policies via maximizing the rewardsprovided by an environment network. The policy network is constructed followinga domain-transition design paradigm, where three modules including pixel-leveltexture complexity evaluation, DCT feature extraction, and mode-wiserearrangement, are proposed. These modules operate in serial, graduallyextracting useful features from a decompressed JPEG image and converting theminto embedding policies for DCT elements, while considering JPEGcharacteristics including inter-block and intra-block correlationssimultaneously. The environment network is designed in a gradient-oriented wayto provide stable reward values by using a wide architecture equipped with afixed preprocessing layer with 8x8 DCT basis filters. Extensive experiments andablation studies demonstrate that the proposed method can achieve good securityperformance for JPEG images against both advanced feature based and modern CNNbased steganalyzers.

Improving Cost Learning for JPEG Steganography by Exploiting JPEG Domain Knowledge