Adversarial Attacks and Defences Competition
Alexey Kurakin, Ian Goodfellow, Samy Bengio, Yinpeng Dong, Fangzhou, Liao, Ming Liang, Tianyu Pang, Jun Zhu, Xiaolin Hu, Cihang Xie, Jianyu Wang,, Zhishuai Zhang, Zhou Ren, Alan Yuille, Sangxia Huang, Yao Zhao, Yuzhe Zhao,, Zhonglin Han, Junjiajia Long, Yerkebulan Berdibekov
TL;DR
This paper describes the structure, organization, and top solutions of the NIPS 2017 adversarial attacks and defenses competition, aiming to advance research in machine learning robustness.
Contribution
It introduces a competitive framework for developing and evaluating adversarial attack and defense methods, fostering progress in this critical area.
Findings
Top solutions demonstrated improved attack techniques.
Defense methods showed increased robustness.
The competition spurred new research directions.
Abstract
To accelerate research on adversarial examples and robustness of machine learning classifiers, Google Brain organized a NIPS 2017 competition that encouraged researchers to develop new methods to generate adversarial examples as well as to develop new ways to defend against them. In this chapter, we describe the structure and organization of the competition and the solutions developed by several of the top-placing teams.
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Taxonomy
TopicsAdversarial Robustness in Machine Learning