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Defensive Distillation is Not Robust to Adversarial Examples
classification
cs.CR
cs.CV
keywords
defensivedistillationadversarialattacksexamplesmisclassificationnetworksneural
read the original abstract
We show that defensive distillation is not secure: it is no more resistant to targeted misclassification attacks than unprotected neural networks.
Forward citations
Cited by 1 Pith paper
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Low Rank Adaptation for Adversarial Perturbation
Adversarial perturbations possess an inherently low-rank structure that enables more efficient and effective black-box adversarial attacks via subspace projection.
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