Search for Image Steganographic Policy With Adversary and Auxiliary Constrained Distance Measure

Abstract

Steganography is to conceal the presence of secret communication. Steganalyser based on rich models and deep learning achieves state-of-the-art performance. However, due to the difficulty of capturing the unknown distribution of the high-dimensional cover, it is challenging to design a steganographic scheme from the view of traditional and deep learning-based steganography to defeat the steganalyser. In this paper, we propose a scheme to search the steganographic policy from scratch with the help of auxiliary constrained distance measure and the adversary. The auxiliary distance measure is provided via similarity evaluation and cover estimator. The response space of distance measures is constrained to provide better performance. Similarity evaluation establishes the distance measure in cover space. The cover estimator is to predict the potential distribution of the cover. Thus it could provide the distance measure in distribution space. The adversary model is to simulate the role of the steganalyser. The steganographic policy is searched from playing the adversarial game against an adversary with the relaxation of constrained response space of auxiliary distance measure. Ultimately, within the searched steganographic policy, the stego objects could be generated by the STC coder. Cover estimator, adversary, and steganographic policy are parameterized via neural networks. Experiments demonstrate that our scheme could achieve an effective modification policy and has competitive security performance compared with traditional and state-of-the-art deep learning-based steganographic methods.