Efficient Provably Secure Linguistic Steganography via Range Coding
This work addresses the trade-off between security and capacity in covert communication for applications like secure messaging, representing a strong specific gain rather than a foundational advancement.
The paper tackles the problem of low embedding capacity in provably secure linguistic steganography by proposing a method using range coding with a rotation mechanism, achieving around 100% entropy utilization and high embedding speeds up to 1554.66 bits/s on GPT-2.
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 been extended 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 baseline methods. Moreover, it achieves high embedding speeds (up to 1554.66 bits/s on GPT-2). The code is available at github.com/ryehr/RRC_steganography.