NAACL 2021long52 citations
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model
Honai Ueoka, Yugo Murawaki, Sadao Kurohashi
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.
BibTeX
@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"
}