Seminario 20/22: David Ríos (Real Academia de Ciencias), Adversarial Machine Learning: Perspectives from Adversarial Risk Analysis

Información

Adversarial Machine Learning (AML) is emerging as a major field aimed at the protection of automated ML systems against security threats. The majority of work in this area has built upon a game-theoretic framework by modelling a conflict between an attacker and a defender. After reviewing game-theoretic approaches to AML, we discuss the benefits that adversarial risk analysis perspectives bring in when defending ML based systems and identify relevant research directions.