<ul><li>Graph neural networks (GNNs) are used for fraud detection, but attacks against GNN-based fraud detectors are understudied.</li><li>This study focuses on multi-target graph injection attacks by fraud gangs aiming to evade detection.</li><li>They propose MonTi, a transformer-based attack model that generates attributes and edges of attack nodes simultaneously.</li><li>Experimental results show that MonTi outperforms existing methods on real-world graphs.</li></ul>

Unveiling the Threat of Fraud Gangs to Graph Neural Networks: Multi-Target Graph Injection Attacks against GNN-Based Fraud Detectors

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