A Near-Imperceptible Disambiguating Approach via Verification for Generative Linguistic Steganography
Ruiyi Yan, Tian Song, Yating Yang · 2024
Generative linguistic steganography aims to embed information into natural language texts to achieve covert transmission. However, currently in most approaches based on subword-supporting language models, the extraction process relies on tokenizing steganographic texts into tokens, which could cause segmentation ambiguity, leading to false results or failures of extraction finally. Despite several existing countermeasures (or disambiguation) that have been proposed, they are based on removing tokens of candidate pools, which render them incompatible from the sights of keeping imperceptibility, potentially incurring safety risks. To avoid it, we focus on tackling segmentation ambiguity with near-integrity of candidate pools. In this paper, we propose a near-imperceptible disambiguating approach via verification for generative linguistic steganography. First, this paper draws an all-case extraction method to obtain possible true extracted results. Further, length verification and checksum verification are presented to filter wrong extracted results caused by segmentation ambiguity. Experiments show that our disam-biguating approach outperforms the existing disambiguating approaches, on various criteria, including about 23.49 % higher embedding capacity, about 23.46 % higher imperceptibility and about 5.73% anti-steganalysis capacity of steganographic texts.