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FraudBench is a multimodal benchmark for detecting AI-generated fraudulent refund evidence and shows that current MLLMs often recognize real-damaged evidence but fail on many fake-damaged subsets, with fake-damage detection rates far below the 50\% baseline on most generator subsets.
This survey examines the protective paradigm that has grown around this intervention point, and finds that most protections are still validated mainly against static or weakly adaptive adversaries, while evidence beyond controlled benchmarks remains scarce.
FedRAM is proposed, a three-step framework that progressively updates two scalar hyperparameters: the task importance weight and the client aggregation coefficient, where the proxy model serves as an intermediate between the local reference model and the global agent model.
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