Early neural approaches
Generating Steganographic Text with LSTMs
First arXiv submission: · ACL 2017 Student Research Workshop
Published version: July 2017. The publication and preprint records below retain their own dates and identifiers.
Uses an LSTM language model and vocabulary bins to generate text carrying a secret bitstream. Experiments on tweets and email explore the trade-off between embedding capacity and text quality.
Relationship to Neural Linguistic Steganography
The 2019 Neural Linguistic Steganography paper cites this earlier neural method and reimplements its block-based encoding with GPT-2 as a baseline.
Scope and limitations
The paper leaves formal evaluation against human judges and steganalysis methods to future work; its qualitative examples alone do not establish statistical undetectability.
Cite this paper
Published version
Tina Fang, Martin Jaggi, Katerina Argyraki. Generating Steganographic Text with LSTMs. Proceedings of ACL 2017, Student Research Workshop, 2017
Published version BibTeX · Published version CSL-JSON
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@inproceedings{fang-2017-publication,
title = {{Generating Steganographic Text with LSTMs}},
author = {Fang, Tina and Jaggi, Martin and Argyraki, Katerina},
year = {2017},
booktitle = {Proceedings of ACL 2017, Student Research Workshop},
pages = {100--106},
publisher = {Association for Computational Linguistics},
url = {https://aclanthology.org/P17-3017/}
}
arXiv preprint
Tina Fang, Martin Jaggi, Katerina Argyraki. Generating Steganographic Text with LSTMs. arXiv:1705.10742. First submission: May 30, 2017.
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{fang-2017-preprint,
title = {{Generating Steganographic Text with LSTMs}},
author = {Fang, Tina and Jaggi, Martin and Argyraki, Katerina},
year = {2017},
publisher = {arXiv},
eprint = {1705.10742v1},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/1705.10742v1}
}
Primary sources
- Published author order, title, venue, July 2017, pages; no DOI listed.
- First arXiv submission May 30, 2017.
- Sections 3–4 describe LSTM/bin encoding and experiments; section 5 identifies future security evaluation.
- Sections 2 and 4 identify Fang et al. as the Block baseline and explain the GPT-2 reimplementation.
- Author repository explicitly identifies itself as code for this paper.
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