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cs.CR2022
Morphence-2.0: Evasion-Resilient Moving Target Defense Powered by Out-of-Distribution Detection
Abderrahmen Amich, Ata Kaboudi, Birhanu Eshete
Evasion attacks against machine learning models often succeed via iterative probing of a fixed target model, whereby an attack that succeeds once will succeed repeatedly. One promi…
cs.CR2022★ 2 cited
MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members
Ismat Jarin, Birhanu Eshete
In membership inference attacks (MIAs), an adversary observes the predictions of a model to determine whether a sample is part of the model's training data. Existing MIA defenses c…
cs.LG2022★ 3 cited
Rethinking Machine Learning Robustness via its Link with the Out-of-Distribution Problem
Abderrahmen Amich, Birhanu Eshete
Despite multiple efforts made towards robust machine learning (ML) models, their vulnerability to adversarial examples remains a challenging problem that calls for rethinking the d…