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Seek-and-Hide: Adversarial Steganography via Deep Reinforcement Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence  (IF16.389),  Pub Date : 2021-09-22, DOI: 10.1109/tpami.2021.3114555
Wenwen Pan,Yanling Yin,Xinchao Wang,Yongcheng Jing,Mingli Song

The goal of image steganography is to hide a full-sized image, termed secret, into another, termed cover. Prior image steganography algorithms can conceal only one secret within one cover. We propose an adaptive local image steganography (AdaSteg) system that allows for scale- and location-adaptive image steganography. By adaptively hiding the secret on a local scale, the proposed system makes the steganography more secured, and further enables multi-secret steganography within one single cover. Specifically, this is achieved via adaptive patch selection stage and secret encryption stage. Given a pair of secret and cover, the optimal local patch for concealment is determined adaptively by exploiting deep reinforcement learning with the proposed steganography quality function and policy network. The secret image is then converted into a patch of encrypted noises, resembling the process of generating adversarial examples, which are further encoded to a local region of the cover to realize a more secured steganography. Furthermore, we propose a novel criterion for the assessment of local steganography and collect a challenging dataset, thus contributing to a standardized benchmark for the area. Experimental results demonstrate that the proposed approach yields results superior to the state of the art in both security and capacity.