On the NHIS mortality task, standard white-box attacks flip the final prediction of the AdaptiveFS RL questionnaire model in 33.1% (FGSM) to 64.7% (AutoAttack) of tested correctly classified cases.
An adaptive testing item selection strategy via a deep reinforcement learning approach,
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Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation
On the NHIS mortality task, standard white-box attacks flip the final prediction of the AdaptiveFS RL questionnaire model in 33.1% (FGSM) to 64.7% (AutoAttack) of tested correctly classified cases.