Text Steganalysis Method Based on Hierarchy-Aware Matching

Existing text steganalysis models experience difficulty in learning and extracting multilayer effective information that truly exists in encrypted data. To address this issue, a text steganalysis method, HAM-Stega, based on hierarchy-aware matching is proposed. This method utilizes the matching relationship between the relative distance between text information and label information in steganographic data to obtain a feature-matching relationship between text and coarse- and fine-grained labels in a hierarchy-aware manner. Based on this, joint embedding and matching learning loss functions are designed to guide the classification of text feature representations and obtain the final hierarchical classification information. The experimental results show that HAM-Stega's detection accuracy on the Large multidistribution mixed dataset, which is similar to real-world scenarios, improves by approximately 1.25—7.42 percentage points compared to the comparison model, indicating that the proposed model has an effective steganalysis detection capability on mixed datasets. Simultaneously, HAM-Stega can extract and detect other layers of effective information present in the steganographic data, such as steganographic algorithms for encrypted text, embedding rates, and corpus types. It improves the hierarchical classification metrics Macro-F1 and Micro-F1 by 5.41 and 4.36 percentage points, respectively, compared with the pretrained BERT model.