  Currently, most methods for text steganalysis are based on deep neural
networks (DNNs). However, in real-life scenarios, obtaining a sufficient amount
of labeled stego-text for correctly training networks using a large number of
parameters is often challenging and costly. Additionally, due to a phenomenon
known as dataset bias or domain shift, recognition models trained on a large
dataset exhibit poor generalization performance on novel datasets and tasks.
Therefore, to address the issues of missing labeled data and inadequate model
generalization in text steganalysis, this paper proposes a cross-domain
stego-text analysis method (PDTS) based on pseudo-labeling and domain
adaptation (unsupervised learning). Specifically, we propose a model
architecture combining pre-trained BERT with a single-layer Bi-LSTM to learn
and extract generic features across tasks and generate task-specific
representations. Considering the differential contributions of different
features to steganalysis, we further design a feature filtering mechanism to
achieve selective feature propagation, thereby enhancing classification
performance. We train the model using labeled source domain data and adapt it
to target domain data distribution using pseudo-labels for unlabeled target
domain data through self-training. In the label estimation step, instead of
using a static sampling strategy, we propose a progressive sampling strategy to
gradually increase the number of selected pseudo-label candidates. Experimental
results demonstrate that our method performs well in zero-shot text
steganalysis tasks, achieving high detection accuracy even in the absence of
labeled data in the target domain, and outperforms current zero-shot text
steganalysis methods.
