LLsM: Generative Linguistic Steganography with Large Language Model

• URL: http://arxiv.org/abs/2401.15656v3
• Date: Mon, 8 Apr 2024 03:50:39 GMT
• Title: LLsM: Generative Linguistic Steganography with Large Language Model
• Authors: Yihao Wang, Ruiqi Song, Ru Zhang, Jianyi Liu, Lingxiao Li,
• Abstract summary: Linguistic Steganography (LS) tasks aim to generate steganographic text (stego) based on secret information. Existing LS methods do not consider the controllable generation of stegos containing specific discourses. This paper proposes the LLsM, the first LS work with the Large Language Model (LLM)
• Score: 10.72286166021398
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Linguistic Steganography (LS) tasks aim to generate steganographic text (stego) based on secret information. Only authorized recipients can perceive the existence of the stegos and extract secrets, thereby preserving privacy. However, existing LS methods do not consider the controllable generation of stegos containing specific discourses such as style, genre, and theme. And they are difficult to simulate high-quality natural texts. As a result, the stegos are easily perceived and detectable, compromising covert communication. This paper proposes the LLsM, the first LS work with the Large Language Model (LLM). Regarding open-source LLMs, we reconstruct the token generator of LLM to the "stego generator" so that it can control the generation of stego based on the secret. In this "stego generator", the candidate pool is encoded by range coding, and the adjustment factor for the interval length is also given. The secret determines the interval, thereby determining the next token. This better simulates the distribution of natural texts and controls the adjustment of the embedding rate. In addition, we preliminarily built an LLsM-c architecture for closed-source LLMs. It encodes discourse to obtain high-quality prompts containing discourse based on secrets, and generates pure natural texts containing discourse. Experiments show that LLsM performs superior to prevalent LS and related-task baselines regarding various kinds of concealment and anti-steganalysis. LLsM's MAUVE surpasses baselines by 60%-80% and anti-steganalysis exceeds baselines by 20%-30%. Notably, LLsM can also generate longer stegos with high quality, showing its advantages in understanding and coherence.

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