An Automatic Cost Learning Framework for Image Steganography Using Deep Reinforcement Learning

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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.
Funding Information
  • NSFC (61872244, U1636202, 61772349)
  • Guangdong Basic and Applied Basic Research Foundation (2019B151502001)
  • Guangdong Research and Development Program in Key Areas (2019B010139003)
  • Shenzhen Research and Development Program (JCYJ20180305124325555, GJHZ20180928155814437)

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