@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model
%A Ueoka, Honai
%A Murawaki, Yugo
%A Kurohashi, Sadao
%Y Toutanova, Kristina
%Y Rumshisky, Anna
%Y Zettlemoyer, Luke
%Y Hakkani-Tur, Dilek
%Y Beltagy, Iz
%Y Bethard, Steven
%Y Cotterell, Ryan
%Y Chakraborty, Tanmoy
%Y Zhou, Yichao
%S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F ueoka-etal-2021-frustratingly
%X 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.
%R 10.18653/v1/2021.naacl-main.433
%U https://aclanthology.org/2021.naacl-main.433
%U https://doi.org/10.18653/v1/2021.naacl-main.433
%P 5486-5492
Markdown (Informal)
[Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model](https://aclanthology.org/2021.naacl-main.433) (Ueoka et al., NAACL 2021)
ACL