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Adversarial Minimax Training for Robustness Against Adversarial Examples

  • Ryota Komiyama
  • Motonobu Hattori
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11302)

Abstract

In this paper, we propose a novel method to improve robustness against adversarial examples. In conventional methods, in order to take measures against adversarial examples, a classifier is learned with adversarial examples generated in a specific way. However, this method can defend against only limited types of adversarial examples. In the proposed method, in order to deal with a wide range of adversarial examples, two networks are used: a generator network and a classifier network. The generator network generates noise to make an adversarial example and the classifier network acquires robustness by learning the adversarial example. Computer simulation results show that the proposed method is more robust against adversarial examples generated by some different methods in black box attacks than the conventional adversarial training methods.

Keywords

Adversarial examples Adversarial training Black box attack 

Notes

Acknowledgments

This work was supported in part by JSPS KAKENHI (Grant no. 16K00329).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Interdisciplinary Graduate School of Medicine, Engineering and AgricultureUniversity of YamanashiKofu, YamanashiJapan

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