CNNs Achieve Adversarial Robustness With Filters
A combination of noise and bilateral filters has been used to achieve supralinear and scalable adversarial robustness in CNNs. This approach addresses the vulnerability of deep neural networks to adversarial examples.
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Sources · 7 independent
Modernity/arxiv
“A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNs.”
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