Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses-Reference-Cited by-同舟云学术

Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses

Author:

Goldblum Micah¹^ORCID,Tsipras Dimitris²,Xie Chulin³^ORCID,Chen Xinyun⁴,Schwarzschild Avi¹,Song Dawn⁴,Madry Aleksander²,Li Bo³,Goldstein Tom¹

Affiliation:

1. University of Maryland, College Park, MD, USA

2. Massachusetts Institute of Technology, Cambridge, MA, USA

3. University of Illinois, Champaign, IL, USA

4. University of California, Berkeley, CA, USA

Funder

Defense Advanced Research Projects Agency

QED4RML

D3M programs

National Science Foundation

Amazon Research Award Program

Berkeley DeepDrive

Facebook PhD Fellowship

ONR MURI Program

Alfred P. Sloan Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Applied Mathematics,Artificial Intelligence,Computational Theory and Mathematics,Computer Vision and Pattern Recognition,Software

Link

Reference205 articles.

1. Influence functions in deep learning are fragile;basu,2020

2. Stronger data poisoning attacks break data sanitization defenses;koh,2018

4. Auditing differentially private machine learning: How private is private SGD?;jagielski;in Proc Adv Neural Informat Process Syst,2020

5. Support vector machines under adversarial label noise;biggio;Proc Asian Conf Mach Learn,2011

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