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.
Limits of Deepfake Detection: A Robust Estimation Viewpoint
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abstract
Deepfake detection is formulated as a hypothesis testing problem to classify an image as genuine or GAN-generated. A robust statistics view of GANs is considered to bound the error probability for various GAN implementations in terms of their performance. The bounds are further simplified using a Euclidean approximation for the low error regime. Lastly, relationships between error probability and epidemic thresholds for spreading processes in networks are established.
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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.