Hiding in Plain Text: A New Framework for Covert Communication
A new study introduces SA-ANS, a self-adaptive framework for linguistic steganography that conceals secret information within natural language text. The method leverages a self-adjusting Asymmetric Numeral System to allow user-specified embedding rates, using probabilistic coding and adaptive candidate selection. This approach dynamically tailors the token pool to the language model’s probability distribution, producing fluent and semantically coherent stego text that is statistically indistinguishable from natural language. Extensive evaluations across multiple benchmark datasets demonstrate that SA-ANS outperforms current state-of-the-art methods in embedding efficiency, linguistic quality, and robustness to steganalysis.
Study Significance: This advancement in linguistic steganography directly impacts secure communication and data privacy applications. For NLP practitioners, it represents a significant step in balancing text generation quality with information-theoretic security, a core challenge in the field. The framework’s adaptability and performance improvements offer practical tools for developing more robust and undetectable covert communication channels.
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