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Article

Perspectives on Adversarial Classification

1
School of Management, University of Shanghai for Science and Technology, Shanghai 201206, China
2
ICMAT-CSIC, 28049 Madrid, Spain
*
Author to whom correspondence should be addressed.
Mathematics 2020, 8(11), 1957; https://doi.org/10.3390/math8111957
Received: 6 September 2020 / Revised: 8 October 2020 / Accepted: 3 November 2020 / Published: 5 November 2020
Adversarial classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). So far, most approaches to AC have followed a classical game-theoretic framework. This requires unrealistic common knowledge conditions untenable in the security settings typical of the AML realm. After reviewing such approaches, we present alternative perspectives on AC based on adversarial risk analysis. View Full-Text
Keywords: classification; adversarial machine learning; security; robustness; adversarial risk analysis classification; adversarial machine learning; security; robustness; adversarial risk analysis
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MDPI and ACS Style

Rios Insua, D.; Naveiro, R.; Gallego, V. Perspectives on Adversarial Classification. Mathematics 2020, 8, 1957. https://doi.org/10.3390/math8111957

AMA Style

Rios Insua D, Naveiro R, Gallego V. Perspectives on Adversarial Classification. Mathematics. 2020; 8(11):1957. https://doi.org/10.3390/math8111957

Chicago/Turabian Style

Rios Insua, David, Roi Naveiro, and Victor Gallego. 2020. "Perspectives on Adversarial Classification" Mathematics 8, no. 11: 1957. https://doi.org/10.3390/math8111957

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