Adversarial Attacks and Defences Competition

Author:

Kurakin Alexey,Goodfellow Ian,Bengio Samy,Dong Yinpeng,Liao Fangzhou,Liang Ming,Pang Tianyu,Zhu Jun,Hu Xiaolin,Xie Cihang,Wang Jianyu,Zhang Zhishuai,Ren Zhou,Yuille Alan,Huang Sangxia,Zhao Yao,Zhao Yuzhe,Han Zhonglin,Long Junjiajia,Berdibekov Yerkebulan,Akiba Takuya,Tokui Seiya,Abe Motoki

Publisher

Springer International Publishing

Reference44 articles.

1. S. Baluja and I. Fischer. Adversarial transformation networks: Learning to generate adversarial examples. 2017.

2. B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli. Evasion attacks against machine learning at test time. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pages 387–402. Springer, 2013.

3. W. Brendel, J. Rauber, and M. Bethge. Decision-based adversarial attacks: Reliable attacks against black-box machine learning models. 2017.

4. J. Buckman, A. Roy, C. Raffel, and I. Goodfellow. Thermometer encoding: One hot way to resist adversarial examples. Submissions to International Conference on Learning Representations, 2018.

5. N. Carlini and D. Wagner. Adversarial examples are not easily detected: Bypassing ten detection methods. In USENIX Workshop on Offensive Technologies, 2017a.

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