WinStega: An Adaptive Robust Enhancement Framework for Generative Linguistic Steganography

Abstract

With the increasing prevalence of surveillance, safeguarding personal privacy has become a critical concern. To protect privacy, various linguistic steganography methods have been developed to conceal private information within seemingly innocuous text for covert communication. However, these methods are highly sensitive to alterations in the stego text, rendering the extraction of secret information impossible if any changes occur, thus limiting their practical application. In this paper, we introduce WinStega, an adaptive and robust linguistic steganography method that employs substring decoding to withstand edit attacks. WinStega embeds secret messages discontinuously using a sliding window approach, incorporating entropy-based constraints to enhance imperceptibility while preserving linguistic quality. This plug-and-play method does not require additional model training and can be implemented during the inference stage, enhancing the robustness of stego texts. Extensive evaluations using three language models demonstrate that WinStega produces high linguistic quality, imperceptible stegotexts and can partially recover secret messages even under adversarial attacks.