TGCA: A Transformer GNN-based Approach with Cross-Attention Mechanism for Steganographic Text Detection in Social Networks

Abstract

Steganalysis aims to detect the presence of concealed information within seemingly normal carriers in network transmissions, playing a crucial role in maintaining cybersecurity. With the rapid development of social networks, steganalysis techniques targeting social network texts have attracted significant interest from researchers in recent years. However, existing steganalysis techniques for social texts generally focus on analyzing the statistical features of the text itself, neglecting the relational features between texts, thereby limiting their detection capabilities. In this paper, we propose a novel text steganalysis feature enhancement method—TGCA. This method considers the relational features between texts by introducing GNN, while utilizing Transformers to expand the receptive field of GNNs and incorporating a cross-attention mechanism to reduce the aggregation of noise, thus mitigating the inherent limitations of GNNs. As a result, TGCA more effectively extracts and integrates textual and topological features, enhancing the model's performance in detecting steganographic texts. Experimental results demonstrate that TGCA outperforms existing methods by better leveraging graph and textual features to identify malicious steganographic texts. Our code is available at https://github.com/PandaaKai/TGCA.