ReLOAD: Using Reinforcement Learning to Optimize Asymmetric Distortion for Additive Steganography.

Recently, the success of non-additive steganography has demonstrated that asymmetric distortion can remarkably improve security performance compared with symmetric cost functions. However, most of current existing additive steganographic methods are still based on symmetric distortion. In this paper, for the first time we optimize asymmetric distortion for additive steganography and propose an A3C (Asynchronous Advantage Actor-Critic) based steganographic framework, called ReLOAD. ReLOAD is composed of an actor and a critic, where the former guides action selection for pixel-wise distortion modulation, and the latter evaluates the performance of modulated distortion. Meanwhile, a reward function that considers embedding effects is proposed to unify the goal of steganography and reinforcement learning, so that the minimization of embedding effects can be achieved by learning secure policy to maximize total rewards. Statistical analysis shows that compared with non-additive steganography, ReLOAD achieves lower change rates and makes embedding traces more consistent with cover image textures. Comprehensive experiments conducted on both hand-crafted feature-based and deep learning-based steganalyzers show that ReLOAD significantly promotes the state-of-the-art security performance of current additive methods and even outperforms non-additive steganography when the modification distribution gets sparser.