CPG-LS: Causal Perception Guided Linguistic Steganography

Lingyun Xiang, Jiali Xia, Yangfan Liu, Yan Gui

Abstract: The current lexical substitution-based linguistic steganography primarily determines the substitutions by their linguistic suitability, overlooking the disparity in their capability against steganalysis. This oversight leads to the potential security risk by performing poor substitutions. To address this issue, this letter proposes a C ausal P erception G uided L inguistic S teganography(CPG-LS) via elaborate and secure lexical substitutions. CPG-LS constructs a causal perception network by making full use of a trained CNN discriminator to assess the security of each original word in the cover text and its substitutable candidates for controlling the embedding of secret message. Particularly, the causal score of each word is comprehensively measured from two perspectives by the causal perception network, one is word saliency, and the other is anti-steganalysis capability. Finally, the selection of original words is explicitly guided by their causal scores for securely embedding secret message. As the causal score of a word decreases, the perturbation caused by modifying this word becomes smaller, leading to stronger anti-steganalysis capability and lower semantic distortion in the obtained stegotexts. The experimental results demonstrate that CPG-LS achieves better text quality and anti-steganalysis capability than existing similar methods.