Perspectives on Adversarial Classification

被引：4

作者：

Insua, David Rios ^{[1
,2
]}

Naveiro, Roi ^{[2
]}

Gallego, Victor ^{[2
]}

机构：

[1] Univ Shanghai Sci & Technol, Sch Management, Shanghai 201206, Peoples R China

[2] CSIC, ICMAT, Madrid 28049, Spain

来源：

MATHEMATICS | 2020年 / 8卷 / 11期

基金：

欧盟地平线“2020”;

关键词：

classification; adversarial machine learning; security; robustness; adversarial risk analysis; RISK ANALYSIS;

D O I：

10.3390/math8111957

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

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.

引用

页码：1 / 21

页数：21

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