@inproceedings{huang-etal-2026-od,
    title = "{OD}-Stega: {LLM}-Based Relatively Secure Steganography via Optimized Distributions",
    author = "Huang, Yu-Shin  and
      Just, Peter  and
      Yin, Hanyun  and
      Narayanan, Krishna  and
      Huang, Ruihong  and
      Tian, Chao",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.36/",
    pages = "827--851",
    ISBN = "979-8-89176-380-7",
    abstract = "We consider coverless steganography where a Large Language Model (LLM) is used to generate stego-texts in combination with arithmeticic coding. An efficient method should embed secret bits in as few language tokens as possible while keeping the stego-text as natural as possible. We show that this problem is equivalent to maximizing the entropy of a replacement probability distribution of the next token generation, subject to a constraint on the divergence between the new distribution and the original one produced by the LLM. A closed-form solution is provided under either the KL divergence or the total variation constraint. Several important practical issues are also tackled: 1) An often-overlooked tokenization mismatch issue is resolved with a simple prompt selection approach, 2) The combination of the optimized distribution and the vocabulary truncation technique is considered, and 3) The incorporation of the proposed approach with existing (potentially non arithemtic coding based) techniques, e.g., the Discop technique."
}