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Improving Adversarial Robustness via Channel-wise Activation Suppressing
[article]
2022
arXiv
pre-print
The study of adversarial examples and their activation has attracted significant attention for secure and robust learning with deep neural networks (DNNs). Different from existing works, in this paper, we highlight two new characteristics of adversarial examples from the channel-wise activation perspective: 1) the activation magnitudes of adversarial examples are higher than that of natural examples; and 2) the channels are activated more uniformly by adversarial examples than natural examples.
arXiv:2103.08307v2
fatcat:lhfljy7pq5g7dm4nbtccbtzloi