LINK: Linguistic Steganalysis Framework with External Knowledge

Linguistic steganalysis is the technology to distinguish whether looking-innocent texts hide covert (possibly hazardous) messages. Traditional methods, dominantly focusing on internal linguistic difference in texts, are seriously challenged by the recent linguistic steganography technology that can reduce the difference to near zero. However, even via the most advanced linguistic steganography methods, due to the random and uncontrollable message bits, steganographic texts may express content against common sense knowledge. To fully employ this defect of linguistic steganography, we propose \textbf{LINK}, a novel \textbf{Lin}guistic steganalysis framework with the help of external \textbf{K}nowledge. We link texts to the external knowledge database, and employ Graph Neural Networks (GNNs) to translate linked knowledge into knowledge features, while linguistic features will be captured by the same modules from existing methods. Knowledge features and linguistic features will be combined to make final decisions. Extensive experimental results show that owing to additional external knowledge, the proposed framework can effectively compensate for the shortcomings of existing methods.