Linguistic Steganalysis via Text Dual Attention Fusing Statistical and Multi-Layer Semantic Features

Linguistic steganalysis faces the challenge of increasingly high-quality stego text, making it difficult to distinguish these text from cover text. The two main issues with current methods are: 1) deep learning models tend to overfit, which hurts their ability to apply to new situations, and 2) feature fusion models don't mix different types of features effectively, leading to poor results. In this letter, we propose a Text Dual Attention linguistic steganalysis method Fusing Statistical and Multi-layer Semantic features (TDA-FSMS). TDA-FSMS firstly extracts multi-layer semantic features using different encoder layers from Enhanced Representation through knowledge integration (ERNIE), combining shallow and deep features to relieve potential overfitting. TDA-FSMS also designs a text dual attention network that simultaneously maps both multi-layer semantic and statistical features into a shared high-dimensional space to bring in a smooth feature fusion. Experimental results show that the text dual attention and the multi-layer semantic fusion enable TDA-FSMS to improve steganalysis performance than existing methods.