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Published in:
2019 | OriginalPaper | Chapter
Mitigating Overfitting Using Regularization to Defend Networks Against Adversarial Examples
Authors : Yoshimasa Kubo, Thomas Trappenberg
Published in: Advances in Artificial Intelligence
Publisher: Springer International Publishing
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Abstract
Recent work has shown that neural networks are vulnerable to adversarial examples. There is an discussion if this problem is related to overfitting. While many researcher stress that overfitting is not related to adversarial sensitivity, Galloway et al. [4] showed that mitigating overfitting improves the accuracy on adversarial examples. In this study we add to this view that overfitting is a factor in adversarial sensitivity. To make this argument, we include two directions in our study, the first is to evaluate several standard regularization techniques with adversarial attacks and to the second is to evaluate binarized stochastic neural networks on adversarial examples. We report that strong regularizations including binarized stochastic neural networks do not only improve overfitting but also help the networks in fighting against adversarial attacks. Supplemental materials are available at https://github.com/ykubo82/ovf.