Zero-shot Generative Linguistic Steganography
Published in
North American Chapter of the Association for C...(2024)External Links:
TL
TL;DR
This paper proposes a novel zero-shot approach based on in-context learning for linguistic steganography to achieve better perceptual and statistical imperceptibility and designs several new metrics and reproducible language evaluations to measure the imperceptibility of the stegotext.
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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