Heterogeneous Domain Remapping for Universal Detection of Generative Linguistic Steganography

Current researchers have proposed various steganalysis methods for detecting secret information within social media texts, which can achieve relatively optimal detection performance in specific steganographic domains. However, considering the practical application of social media, we can only obtain the text to be tested without prior knowledge of the steganographic domain it belongs to. Consequently, we are unable to prepare a supervised training dataset in advance. This places higher demands on steganalysis algorithms, necessitating their ability to generalize and detect any unknown steganography domain. To this end, we propose a universal detection method for generative linguistic steganography based on heterogeneous domain remapping. The core idea is to employ a neural structure composed of pre-trained embedding layers and capsule networks to extract steganography-sensitive correlation features. Subsequently, the concept of contrastive learning is utilized to remap the sensitive features from heterogeneous steganography domains into a unified domain. This process effectively extracts domain-invariant features, thereby enabling the detection of unknown steganographic domains. Experimental results demonstrate that the proposed method outperforms existing approaches by an average of over 2% across various steganography domains.