Addressing Segmentation Ambiguity in Neural Linguistic Steganography

Jumon Nozaki, Yugo Murawaki


Abstract
Previous studies on neural linguistic steganography, except Ueoka et al. (2021), overlook the fact that the sender must detokenize cover texts to avoid arousing the eavesdropper’s suspicion. In this paper, we demonstrate that segmentation ambiguity indeed causes occasional decoding failures at the receiver’s side. With the near-ubiquity of subwords, this problem now affects any language. We propose simple tricks to overcome this problem, which are even applicable to languages without explicit word boundaries.
Anthology ID:
2022.aacl-short.15
Volume:
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2022
Address:
Online only
Venues:
AACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
109–116
Language:
URL:
https://aclanthology.org/2022.aacl-short.15
DOI:
Bibkey:
Cite (ACL):
Jumon Nozaki and Yugo Murawaki. 2022. Addressing Segmentation Ambiguity in Neural Linguistic Steganography. In Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 109–116, Online only. Association for Computational Linguistics.
Cite (Informal):
Addressing Segmentation Ambiguity in Neural Linguistic Steganography (Nozaki & Murawaki, AACL-IJCNLP 2022)
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PDF:
https://aclanthology.org/2022.aacl-short.15.pdf