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Found 3 papers, 2 papers with code
Transferable Availability Poisoning Attacks
We consider availability data poisoning attacks, where an adversary aims to degrade the overall test accuracy of a machine learning model by crafting small perturbations to its training data.
Membership Inference Attacks by Exploiting Loss Trajectory
Machine learning models are vulnerable to membership inference attacks in which an adversary aims to predict whether or not a particular sample was contained in the target model's training dataset.
Auditing Membership Leakages of Multi-Exit Networks
Furthermore, we propose a hybrid attack that exploits the exit information to improve the performance of existing attacks.
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