Image Semantic Steganography: A Way to Hide Information in Semantic Communication

Yanhao Huo, Shijun Xiang, Xiangyang Luo, Xinpeng Zhang

Abstract: Semantic communication (SC) is an emerging communication paradigm that transmits only task-related semantic features to receivers, offering advantages in speed. However, existing robust steganography cannot extract message correctly after SC. To address this issues, we propose a novel steganography framework based on Generating Adversarial Networks (GANs) for SC, called “Image Semantic Steganography”. Our framework embeds message into semantic features to guarantee extraction while considering both pixel-level and semantic-level distortions to enhance security. Experimental results show that our framework not only achieves message extraction successfully and behavioral covertness during and after SC, but also does not impact the implementation of SC.