PCA as a defense against some adversaries
Neural network classifiers are known to be highly vulnerable to adversarial perturbations in their inputs. Under the hypothesis that adversarial examples lie outside of the sub-manifold of natural images, previous work has investigated the impact of principal components in data on adversarial robust...
| Main Authors: | , , |
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| Format: | Article |
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Center for Brains, Minds and Machines (CBMM)
2022
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| Online Access: | https://hdl.handle.net/1721.1/141424 |