Efficient Provably Secure Linguistic Steganography via Range Coding

ACL ARR 2025 May Submission1338 Authors

Abstract:

Linguistic steganography involves embedding secret messages within seemingly innocuous texts to enable covert communication. Provable security, which is a long-standing goal and key motivation, has become adaptive to language-model-based steganography.
Previous provably secure approaches have achieved perfect imperceptibility, measured by zero Kullback–Leibler (KL) divergence, but at the expense of embedding capacity.
In this paper, we attempt to directly use a classic entropy coding method (**range coding**) to achieve secure steganography, and then propose an efficient and provably secure linguistic steganographic method with a rotation mechanism.
Experiments across various language models show that our method achieves around 100\% entropy utilization (embedding efficiency) for embedding capacity, outperforming the existing provably secure methods. Moreover, it delivers high speeds (up to 1554.66 bits/s on GPT-2).

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: security/privacy

Contribution Types: NLP engineering experiment, Approaches to low-resource settings, Approaches low compute settings-efficiency, Publicly available software and/or pre-trained models, Data analysis, Theory

Languages Studied: English

Keywords: security/privacy, linguistic steganography

Submission Number: 1338