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.

本文提出了一种基于伪标签和领域适应（无监督学习）的跨域文本隐写分析方法（PDTS），通过使用预训练的 BERT 和单层 Bi-LSTM 来学习和提取任务间通用特征，并生成特定任务表示。通过选择性特征传播来增强分类性能，并通过自训练使用未标记的目标领域数据和伪标签来训练模型。实验结果表明，我们的方法在零样本文本隐写分析任务中表现出色，在没有目标域标记数据的情况下实现高检测准确率，并优于当前的零样本文本隐写分析方法。