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ReFace: Real-time Adversarial Attacks on Face Recognition Systems
We find that the white-box attack success rate of a pure U-Net ATN falls substantially short of gradient-based attacks like PGD on large face recognition datasets.
Privacy Leakage Avoidance with Switching Ensembles
We consider membership inference attacks, one of the main privacy issues in machine learning.
Membership Model Inversion Attacks for Deep Networks
With the increasing adoption of AI, inherent security and privacy vulnerabilities formachine learning systems are being discovered.
Subspace Methods That Are Resistant to a Limited Number of Features Corrupted by an Adversary
In this paper, we consider batch supervised learning where an adversary is allowed to corrupt instances with arbitrarily large noise.
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