Abstract
The generative linguistic steganography in social networks have potential huge abuse and regulatory risks, with serious implications for information security, especially in the era of large language models. Many works have explored detecting steganographic texts with progressively enhanced imperceptibility, but they can only achieve poor performance in real social network scenarios. One key reason is that these methods primarily focus on linguistic features, which are extremely insufficient owing to the fragmentation of social texts. In this paper, we propose a novel method called CATS (Connection-aware and interAction-based Text Steganalysis) to effectively detected the potentially malicious steganographic texts. CATS captures social networks connection information by graph representation learning, enhances linguistic features by contrastive learning and fully integrates features above via a novel features interaction module. Our experimental results demonstrate that CATS outperforms existing methods by exploiting social network graph structure features and interactions in social network environments.
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This work was supported by the National Natural Science Foundation of China under Grant U1936216 and Grant 61862002.
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Pang, K. et al. (2024). CATS: Connection-Aware and Interaction-Based Text Steganalysis in Social Networks. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14451. Springer, Singapore. https://doi.org/10.1007/978-981-99-8073-4_9
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