ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense-Reference-Cited by-同舟云学术

ASK: Adversarial Soft k-Nearest Neighbor Attack and Defense

Author:

Wang Ren¹^ORCID,Chen Tianqi²^ORCID,Yao Philip¹,Liu Sijia³,Rajapakse Indika⁴^ORCID,Hero Alfred O.¹^ORCID

Affiliation:

1. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA

2. Department of Statistics, University of Michigan, Ann Arbor, MI, USA

3. Department of Computer Science and Engineering, Michigan State University, East Lansing, MI, USA

4. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

Funder

United States Department of Defense, Defense Advanced Research Projects Agency

Army Research Office

Department of Energy National Nuclear Security Administration

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

Reference27 articles.

1. Deep k-nearest neighbors: Towards confident, interpretable and robust deep learning;papernot;arXiv 1803 04765,2018

3. Deep k-NN for noisy labels;bahri;Proc Int Conf Mach Learn,2020

5. AdvKnn: Adversarial attacks on K-nearest neighbor classifiers with approximate gradients;li;arXiv 1911 06591,2019

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