@inproceedings{ueoka-etal-2021-frustratingly,
    title = "Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model",
    author = "Ueoka, Honai  and
      Murawaki, Yugo  and
      Kurohashi, Sadao",
    editor = "Toutanova, Kristina  and
      Rumshisky, Anna  and
      Zettlemoyer, Luke  and
      Hakkani-Tur, Dilek  and
      Beltagy, Iz  and
      Bethard, Steven  and
      Cotterell, Ryan  and
      Chakraborty, Tanmoy  and
      Zhou, Yichao",
    booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
    month = jun,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2021.naacl-main.433/",
    doi = "10.18653/v1/2021.naacl-main.433",
    pages = "5486--5492",
    abstract = "With advances in neural language models, the focus of linguistic steganography has shifted from edit-based approaches to generation-based ones. While the latter`s payload capacity is impressive, generating genuine-looking texts remains challenging. In this paper, we revisit edit-based linguistic steganography, with the idea that a masked language model offers an off-the-shelf solution. The proposed method eliminates painstaking rule construction and has a high payload capacity for an edit-based model. It is also shown to be more secure against automatic detection than a generation-based method while offering better control of the security/payload capacity trade-off."
}