KIKE: Linguistic Steganalysis Based on Knowledge Infusion and Knowledge Encoding

Abstract

Efficient detection of steganographic text in public networks is critical for maintaining cyberspace security. Current text steganalysis algorithms focus on improving feature extraction models but face challenges with fragmented network texts in real-world environments, limiting their practical use. To address this, we propose a novel linguistic steganalysis method called KIKE, which integrates Knowledge Infusion and Knowledge Encoding. KIKE utilizes knowledge graphs to enhance semantic feature extraction and employs graph neural networks for cognitive verification. Experimental results show that KIKE significantly improves detection performance, offering practical value in information security.