Extensions and reliability
Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding
First arXiv submission: · EMNLP 2020
Published version: November 2020. The publication and preprint records below retain their own dates and identifiers.
Introduces self-adjusting arithmetic coding, choosing the candidate-set size at each generation step to control divergence from the language model. It supplies an imperceptibility analysis and evaluates capacity, divergence, and human judgments across multiple datasets.
Relationship to Neural Linguistic Steganography
The introduction explicitly states that SAAC builds on Ziegler, Deng, and Rush's 2019 arithmetic-coding method, replacing a fixed truncation size with an adaptive one.
Scope and limitations
Its distributional guarantee is relative to the underlying language model; choosing the truncation threshold still trades embedding behavior and computation against divergence.
Cite this paper
Published version
Jiaming Shen, Heng Ji, Jiawei Han. Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), 2020
Published version BibTeX · Published version CSL-JSON
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@inproceedings{saac-publication,
title = {{Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding}},
author = {Shen, Jiaming and Ji, Heng and Han, Jiawei},
year = {2020},
booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
doi = {10.18653/v1/2020.emnlp-main.22},
pages = {303--313},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/2020.emnlp-main.22/}
}
arXiv preprint
Jiaming Shen, Heng Ji, Jiawei Han. Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding. arXiv:2010.00677. First submission: October 1, 2020.
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.
arXiv preprint BibTeX · arXiv preprint CSL-JSON
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@misc{saac-preprint,
title = {{Near-imperceptible Neural Linguistic Steganography via Self-Adjusting Arithmetic Coding}},
author = {Shen, Jiaming and Ji, Heng and Han, Jiawei},
year = {2020},
publisher = {arXiv},
eprint = {2010.00677v1},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2010.00677v1}
}
Primary sources
- Published bibliographic metadata and DOI.
- First arXiv submission October 1, 2020.
- Introduction explicitly identifies extension of 2019 method; sections 2–3 define relative imperceptibility/adaptive K; page 1 links code.
- Official code repository linked by the paper.
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