ADVERSARIAL ATTACKS ON FRAUD DETECTION MODELS: A REVIEW OF EVASION, DATA POISONING, AND CAMOUFLAGED FRAUDSTERS
activity, but the same graph structure gives adversaries ways to mislead them. This narrative review examines adversarial attacks on fraud detection models, with a focus on GNN-based systems. It groups attacks into evasion attacks at inference time, data poisoning attacks at training time, and camouflage, in which frau...