Extensions and reliability
Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models
First arXiv submission: · EMNLP 2025
Published version: November 2025. The publication and preprint records below retain their own dates and identifiers.
Studies tokenization inconsistencies between generated token sequences and the text a receiver retokenizes. It proposes stepwise verification for steganography and post-hoc rollback for watermark detection, evaluating how these changes affect reliability, text quality, and detection.
Relationship to Neural Linguistic Steganography
Section 4.1 explicitly uses Ziegler et al.'s arithmetic coding in all steganography experiments, extending the reliability layer around the 2019 approach rather than replacing its coding principle.
Scope and limitations
Steganographic extraction assumes an attacker does not modify the text. The authors report smaller watermarking gains because inconsistent tokens are infrequent and note that a complete theory of the inconsistency mechanism remains open.
Cite this paper
Published version
Ruiyi Yan, Yugo Murawaki. Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, 2025
Published version BibTeX · Published version CSL-JSON
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@inproceedings{tokenization-publication,
title = {{Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models}},
author = {Yan, Ruiyi and Murawaki, Yugo},
year = {2025},
booktitle = {Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing},
doi = {10.18653/v1/2025.emnlp-main.361},
pages = {7076--7098},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2025.emnlp-main.361/}
}
arXiv preprint
Ruiyi Yan, Yugo Murawaki. Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models. arXiv:2508.20718. First submission: August 28, 2025.
This citation records the first arXiv version and its original title. The version-specific link distinguishes it from later revisions; cite the version supporting your claim.
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@misc{tokenization-preprint,
title = {{Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models}},
author = {Yan, Ruiyi and Murawaki, Yugo},
year = {2025},
publisher = {arXiv},
eprint = {2508.20718v1},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2508.20718v1}
}
Primary sources
- Full authors, November 2025 venue metadata, pages and DOI.
- First submission August 28, 2025.
- Page 1 links code; sections 2–4 describe methods; section 4.1 explicitly uses Ziegler et al. arithmetic coding; Limitations states threat model and scope of improvement.
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