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.
Anthology ID:
2021.naacl-main.433
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5486–5492
Language:
URL:
https://www.aclweb.org/anthology/2021.naacl-main.433
DOI:
10.18653/v1/2021.naacl-main.433
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PDF:
https://aclanthology.org/2021.naacl-main.433.pdf