Submitted:
06 September 2020
Posted:
08 September 2020
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Abstract
Adversarial Classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). Most approaches to AC so far 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.
Keywords:
Classification
; Adversarial Machine Learning
; Security
; Robustness
; Adversarial Risk Analysis
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