Linguistic Steganalysis Based on Few-Shot Adversarial Training

Linguistic steganalysis is a technique to distinguish whether a text carrier contains secret information via statistical features. Current state-of-the-art methods are caught in two constraints. First, they cannot make accurate predictions on unlearned text distributions. In other words, the performance relies on the consistency of the training and testing distributions. Second, sufficient samples are required to fine-tune these models to reach their optimal states. In this article, we break through these obstacles by developing an effective steganalysis framework in a few-shot scenario. We first build the meta-datasets to simulate the real-world steganalysis environment that contains multi-distributional source and target domains with sparse target-domain samples. Then we propose a few-shot linguistic steganalysis framework combined with an adversarial meta-training mechanism to learn task-transferable features from source task sets to target tasks. Extensive experiments conducted on benchmark datasets show our model has a stable capability to learn transferable knowledge in detecting steganalysis tasks with extremely few-shot samples. We also validate the effectiveness of the model through multi-class steganalysis experiments to identify extra steganographic information involving embedding algorithms and capacities. Our proposed framework is effectively demonstrated to compensate for the drawback of state-of-the-art methods and tremendously improve the detection performance.