2 citations · 5 across the 6 of their papers we have counts for
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cs.LG2021★ 2 cited
A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks
Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon
Adversarial examples for neural network image classifiers are known to be transferable: examples optimized to be misclassified by a source classifier are often misclassified as wel…
cs.LG2021★ 1 cited
Adversarial Perturbations Are Not So Weird: Entanglement of Robust and Non-Robust Features in Neural Network Classifiers
Jacob M. Springer, Melanie Mitchell, Garrett T. Kenyon
Neural networks trained on visual data are well-known to be vulnerable to often imperceptible adversarial perturbations. The reasons for this vulnerability are still being debated…