Safe Machine Learning and Defeating Adversarial Attacks

被引：23
|
作者：
Rouhani, Bita Darvish [1 ]
Samragh, Mohammad [2 ]
Javidi, Tara [2 ]
Koushanfar, Farinaz [2 ]
机构：
[1] Univ Calif San Diego, La Jolla, CA 92093 USA
[2] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA
来源：
IEEE SECURITY & PRIVACY | 2019年 / 17卷 / 02期
基金：
美国国家科学基金会;
关键词：
16;
D O I：
10.1109/MSEC.2018.2888779
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Adversarial attacks have exposed the unreliability of machine-learning (ML) models for decision making in autonomous agents. This article discusses recent research for ML model assurance in the face of adversarial attacks.
引用
收藏
页码：31 / 38
页数：8
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