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Improving Adversarial Robustness to Sensitivity and Invariance Attacks with Deep Metric Learning. (arXiv:2211.02468v1 [cs.LG])
Nov. 7, 2022, 2:20 a.m. | Anaelia Ovalle, Evan Czyzycki, Cho-Jui Hsieh
cs.CR updates on arXiv.org arxiv.org
Intentionally crafted adversarial samples have effectively exploited
weaknesses in deep neural networks. A standard method in adversarial robustness
assumes a framework to defend against samples crafted by minimally perturbing a
sample such that its corresponding model output changes. These sensitivity
attacks exploit the model's sensitivity toward task-irrelevant features.
Another form of adversarial sample can be crafted via invariance attacks, which
exploit the model underestimating the importance of relevant features. Previous
literature has indicated a tradeoff in defending against both attack …
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