Papers › An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning
An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning
Weixuan Tang, Bin Li, Mauro Barni, Jin Li, Jiwu Huang
Abstract— Automatic cost learning for steganography based on deep neural networks is receiving increasing attention. Steganographic methods under such a framework have been shown to achieve better security performance than methods adopting hand-crafted costs. However, they still exhibit some limitations that prevent a full exploitation of their potentiality, including using a function-approximated neural-network-based embedding simulator and a coarse-grained optimization objective without explicitly using pixel-wise information. In this article, we propose a new embedding cost learning framework called SPAR-RL (Steganographic Pixel-wise Actions and Rewards with Reinforcement Learning) that overcomes the above limitations. In SPAR-RL, an agent utilizes a policy network which decomposes the embedding process into pixel-wise actions and aims at maximizing the total rewards from a simulated steganalytic environment, while the environment employs an environment network for pixel-wise reward assignment. A sampling process is utilized to emulate the message embedding of an optimal embedding simulator. Through the iterative interactions between the agent and the environment, the policy network learns a secure embedding policy which can be converted into pixel-wise embedding costs for practical message embedding. Experimental results demonstrate that the proposed framework achieves state-of-the-art security performance against various modern steganalyzers, and outperforms existing cost learning frameworks with regard to learning stability and efficiency.
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