3 accepted papers
In this paper, we present the first detailed analysis of how training hyperparameters---such as learning rate, weight decay, momentum, and batch size---influence robustness against both transfer-based and query-based attacks. Supported by theory and experiments, our study spans a variety of practica
Although distributed machine learning (distributed ML) is gaining considerable attention in the community, prior works have independently looked at instances of distributed ML in either the training or the inference phase. No prior work has examined the combined robustness stemming from distributing…
Although promising, existing defenses against query-based attacks share a common limitation: they offer increased robustness against attacks at the price of a considerable accuracy drop on clean samples. In this work, we show how to efficiently establish, at test-time, a solid tradeoff between robus…