Mitigating Adversarial Effects Through Randomization

ICLR 2018 Cihang Xie • Jianyu Wang • Zhishuai Zhang • Zhou Ren • Alan Yuille

Convolutional neural networks have demonstrated high accuracy on various tasks in recent years. However, they are extremely vulnerable to adversarial examples. For example, imperceptible perturbations added to clean images can cause convolutional neural networks to fail.

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