Deep Reinforcement Learning-Based DCT Image Steganography

Rongjian Yang, Lixin Liu, Bin Han and Feng Hu ()
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Rongjian Yang: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Lixin Liu: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
Bin Han: School of Communications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
Feng Hu: College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

Mathematics, 2025, vol. 13, issue 19, 1-19

Abstract: In this article, we present a novel reinforcement learning-based framework in the discrete cosine transform to achieve better image steganography. First, the input image is divided into several blocks to extract semantic and structural features, evaluating their suitability for data embedding. Second, the Proximal Policy Optimization algorithm (PPO) is introduced in the block selection process to learn adaptive embedding policies, which effectively balances image fidelity and steganographic security. Moreover, the Deep Q-network (DQN) is used for adaptively adjusting the weights of the peak signal-to-noise ratio, structural similarity index, and detection accuracy in the reward formulation. Experimental results on the BOSSBase dataset confirm the superiority of our framework, achieving both lower detection rates and higher visual quality across a range of embedding payloads, particularly under low-bpp conditions.

Keywords: image steganography; deep reinforcement learning; dct block selection; adaptive reward adjustment (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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