Domain Adaptational Steganographic Text Detection Using Few-Shot Adversary-Refinement Framework

Text steganography involves discreetly concealing sensitive messages within natural text, while text steganalysis serves as its counterpart by aiming to detect suspicious text that may contain embedded secret information. Detecting steganographic text has become increasingly difficult because evolving steganographic algorithms produce ever-changing text distributions. Consequently, few-shot text steganalysis, which identifies steganographic text with scarce examples regardless of its distribution has become a research hotspot. The state-of-the-art few-shot text steganalysis relies on the inter-class variance between classes, i.e., they behave satisfactorily in detecting large-variance classes while being incompetent in distinguishing confusable samples from similar steganographic settings. In this paper, we propose an Adversary-Refinement Framework for Text Steganalysis, namely ARTS, which employs a task-invariant extractor and a task-relevant projector to implement an “attract and repel” process. Specifically, in the “attract” stage, we align task-invariant features through adversarial training to shorten the intra-class distance. Afterward, the refined prototypes are projected to a new space in the “repel” stage, and then a refined penalty item is applied to enlarge the inter-class distance. Extensive experiments conducted in six datasets with different inter-class variances demonstrate the superiority of the proposed model over the SOTA models.