Making machine learning robust against adversarial inputs-Reference-Cited by-同舟云学术

Making machine learning robust against adversarial inputs

Published:2018-06-25 Issue:7 Volume:61 Page:56-66
ISSN:0001-0782
Container-title:Communications of the ACM
language:en
Short-container-title:Commun. ACM

Author:

Goodfellow Ian¹,McDaniel Patrick²,Papernot Nicolas³

Affiliation:

1. Google Brain, Mountain View, CA

2. Pennsylvania State University, University Park, PA

3. Penn State University, University Park, PA

Abstract

Such inputs distort how machine-learning-based systems are able to function in the world as it is.

Funder

ARL

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science

Link

https://dl.acm.org/doi/pdf/10.1145/3134599

Reference35 articles.

1. Drebin: Effective and explainable detection of Android malware in your pocket. In Proceedings of the NDSS Symposium (San Diego, CA, Feb.). Internet Society, Reston;Arp D.;VA,2014

2. Can machine learning be secure?

3. Statistical fraud detection: A review;Bolton R.J.;Statistical Science,2002

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