A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models-Reference-Cited by-同舟云学术

A Tale of Evil Twins: Adversarial Inputs versus Poisoned Models

Published:2020-10-30 Issue: Volume: Page:
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Container-title:Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security
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Author:

Pang Ren¹,Shen Hua¹,Zhang Xinyang¹,Ji Shouling²,Vorobeychik Yevgeniy³,Luo Xiapu⁴,Liu Alex⁵,Wang Ting¹

Affiliation:

1. Pennsylvania State University, State College, PA, USA

2. Zhejiang University, Hangzhou, UNK, China

3. Washington University in St. Louis, St. Louis, MO, USA

4. Hong Kong Polytechnic University, Hongkong, UNK, Hong Kong

5. Ant Financia, Hangzhou, UNK, China

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3372297.3417253

Reference55 articles.

1. Wild patterns: Ten years after the rise of adversarial machine learning

2. M. Bojarski D. Del Testa D. Dworakowski B. Firner B. Flepp P. Goyal L. D. Jackel M. Monfort U. Muller J. Zhang X. Zhang J. Zhao and K. Zieba. 2016. End to End Learning for Self-Driving Cars. ArXiv e-prints (2016). M. Bojarski D. Del Testa D. Dworakowski B. Firner B. Flepp P. Goyal L. D. Jackel M. Monfort U. Muller J. Zhang X. Zhang J. Zhao and K. Zieba. 2016. End to End Learning for Self-Driving Cars. ArXiv e-prints (2016).

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