A GAN-based adversarial attack is claimed to achieve 99% success in making fraudulent insurance claims look legitimate to LSTM and XGBoost fraud detectors, but the evidence is undermined by missing dataset and implementation details.
Adversarial attack vulnerability of medical image analysis systems: Unexplored factors.Medical Image Analysis, 73:102141, 2021
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An Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
A GAN-based adversarial attack is claimed to achieve 99% success in making fraudulent insurance claims look legitimate to LSTM and XGBoost fraud detectors, but the evidence is undermined by missing dataset and implementation details.