Machine Learning Security in Industry: A Quantitative Survey-Reference-Cited by-同舟云学术

Machine Learning Security in Industry: A Quantitative Survey

Author:

Grosse Kathrin¹^ORCID,Bieringer Lukas²^ORCID,Besold Tarek R.³^ORCID,Biggio Battista⁴^ORCID,Krombholz Katharina⁵

Affiliation:

1. EPFL, Lausanne, Switzerland

2. QuantPi, Saarbrücken, Germany

3. Department of Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, Netherlands

4. Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy

5. CISPA Helmholtz Center of Information Security, Saarbrücken, Germany

Funder

Fondazione di Sardegna

?sterreichische Forschungsf?rderungsgesellschaft

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

Reference43 articles.

1. On adaptive attacks to adversarial example defenses;tramer;Proc Adv Neural Inf Process Syst,2020

2. Membership inference attacks against machine learning models;shokri;arXiv 1610 05820,2016

4. Motivating the rules of the game for adversarial example research;gilmer;arXiv 1807 06732,2018

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