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cs.LG2023
Provably Cost-Sensitive Adversarial Defense via Randomized Smoothing
Yuan Xin, Dingfan Chen, Michael Backes +1
As ML models are increasingly deployed in critical applications, robustness against adversarial perturbations is crucial. While numerous defenses have been proposed to counter such…
cs.CR2023★ 1 cited
Transferable Availability Poisoning Attacks
Yiyong Liu, Michael Backes, Xiao Zhang
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 tra…
cs.LG2023
Generating Less Certain Adversarial Examples Improves Robust Generalization
Minxing Zhang, Michael Backes, Xiao Zhang
This paper revisits the robust overfitting phenomenon of adversarial training. Observing that models with better robust generalization performance are less certain in predicting ad…