Linguistic steganalysis is an important topic in the field of information security and signal processing. In recent years, linguistic steganalysis have mainly utilized deep learning techniques and make great success. But suffer from the following major disadvantages. From the perspective of model structure, current methods only extract coarse features of the text, without focusing on the fine-grained representations. In terms of application, most of the studies only focus on single hidden scene and ignore the more realistic mixed hidden scenes which are more complex and realistic. These weaknesses limit the performance and the application of linguistic steganalysis in reality. In this letter, we propose a novel linguistic steganalysis method to overcome these weaknesses. This proposed method can extract distinguished text representation which fuses hierarchical features and perform excellently in sophisticated conditions. Firstly, we adapt gated graph neural networks as the coarse graph updater to update node representations on the graph level. Then we design a fine graph updater composed of the graph attention mechanism to focus on the highlighted nodes on the node-level. Moreover, we extract the most notable feature on the dimension-level of node by the graph channel attention module. Finally, the readout function is designed to fuse the hierarchical features and make the classification. The experimental results show that our method achieves the best results compared with the previous methods in both single hidden scene and mixed hidden scenes, which prove the effectiveness of the proposed method.

中文翻译：

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HGA：利用图和注意力机制进行分层特征提取，用于语言隐写分析


语言隐写分析是信息安全和信号处理领域的一个重要课题。近年来，语言隐写分析主要利用深度学习技术并取得了巨大成功。但存在以下主要缺点。从模型结构的角度来看，当前的方法仅提取文本的粗特征，而没有关注细粒度的表示。在应用方面，大多数研究只关注单一隐藏场景，而忽略了更真实、更复杂、更真实的混合隐藏场景。这些弱点限制了语言隐写分析在现实中的性能和应用。在这封信中，我们提出了一种新颖的语言隐写分析方法来克服这些弱点。该方法可以提取融合层次特征的独特文本表示，并在复杂条件下表现出色。首先，我们采用门控图神经网络作为粗图更新器来更新图级别的节点表示。然后，我们设计了一个由图注意机制组成的精细图更新器，以关注节点级别上突出显示的节点。此外，我们通过图通道注意模块提取节点维度级别上最显着的特征。最后，设计读出函数来融合层次特征并进行分类。实验结果表明，与之前的方法相比，我们的方法在单一隐藏场景和混合隐藏场景中都取得了最好的结果，证明了该方法的有效性。