[:es]Título: Adversarial Machine Learning: Perspectives from Adversarial Risk Analysis
Ponente: David Rios (Real Academia de Ciencias e ICMAT)
Organizador: Joaquín Sánchez Soriano
Date: Martes 3 de noviembre de 2020 a las 12:00
[button link=»https://youtu.be/spD-Ctbr_qw» color=»green»] PINCHA AQUÍ PARA VER EL VÍDEO[/button]
ABSTRACT: 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.
[:]