Minimizing Distortion in Steganography via Adaptive Language Model Tuning

Linguistic steganography, a technique that hides secret information within normal text, possesses tremendous potential in various applications such as protecting user privacy. However, previous research in linguistic steganography has primarily focused on adjusting the probability distribution of steganographic text (stegotext) to minimize the difference with text generated by language models, thereby achieving indistinguishability between the two. Nonetheless, the significant gap between real text and generated text has often been overlooked. To address this issue, this paper proposes an innovative method: using an adaptive model tuning strategy, the generated stegotext becomes statistically closer to real text. We leverage a well-trained classifier in conjunction with a fundamental generative language model to produce stegotext that aligns closely with the distribution of real text. Consequently, we gain better control over the distortion between the stegotext and real text, while effectively embedding secret information. Compared to traditional methods, our approach reduces Kullback-Leibler divergence and steganography detection rates, demonstrating its enhanced effectiveness.