Feature Denoising for Improving Adversarial Robustness

9 Dec 2018Cihang Xie • Yuxin Wu • Laurens van der Maaten • Alan Yuille • Kaiming He

This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end.

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