@inproceedings{lin-etal-2024-zero,
title = "Zero-shot Generative Linguistic Steganography",
author = "Lin, Ke and
Luo, Yiyang and
Zhang, Zijian and
Ping, Luo",
editor = "Duh, Kevin and
Gomez, Helena and
Bethard, Steven",
booktitle = "Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.naacl-long.289",
pages = "5168--5182",
abstract = "Generative linguistic steganography attempts to hide secret messages into covertext. Previous studies have generally focused on the statistical differences between the covertext and stegotext, however, ill-formed stegotext can readily be identified by humans. In this paper, we propose a novel zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility. We also design several new metrics and reproducible language evaluations to measure the imperceptibility of the stegotext. Our experimental results indicate that our method produces $1.926\times$ more innocent and intelligible stegotext than any other method.",
}
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<abstract>Generative linguistic steganography attempts to hide secret messages into covertext. Previous studies have generally focused on the statistical differences between the covertext and stegotext, however, ill-formed stegotext can readily be identified by humans. In this paper, we propose a novel zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility. We also design several new metrics and reproducible language evaluations to measure the imperceptibility of the stegotext. Our experimental results indicate that our method produces 1.926\times more innocent and intelligible stegotext than any other method.</abstract>
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%0 Conference Proceedings
%T Zero-shot Generative Linguistic Steganography
%A Lin, Ke
%A Luo, Yiyang
%A Zhang, Zijian
%A Ping, Luo
%Y Duh, Kevin
%Y Gomez, Helena
%Y Bethard, Steven
%S Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F lin-etal-2024-zero
%X Generative linguistic steganography attempts to hide secret messages into covertext. Previous studies have generally focused on the statistical differences between the covertext and stegotext, however, ill-formed stegotext can readily be identified by humans. In this paper, we propose a novel zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility. We also design several new metrics and reproducible language evaluations to measure the imperceptibility of the stegotext. Our experimental results indicate that our method produces 1.926\times more innocent and intelligible stegotext than any other method.
%U https://aclanthology.org/2024.naacl-long.289
%P 5168-5182
Markdown (Informal)
[Zero-shot Generative Linguistic Steganography](https://aclanthology.org/2024.naacl-long.289) (Lin et al., NAACL 2024)
ACL
- Ke Lin, Yiyang Luo, Zijian Zhang, and Luo Ping. 2024. Zero-shot Generative Linguistic Steganography. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5168–5182, Mexico City, Mexico. Association for Computational Linguistics.