Devdiscourse

Adversarial attacks hit financial AI twice as hard during economic stress

The study, titled Conditional Adversarial Fragility in Financial Machine Learning under Macroeconomic Stress, published as a ...
mccormick.northwestern.edu

Exploring New Directions on Robustness in Machine Learning

On November 16, IDEAL hosted a workshop focused on new directions on robustness in machine learning as part of the fall 2021 special quarter. Machine learning systems are widely deployed to facilitate ...
National Academies of Sciences%2c Engineering%2c and Medicine

Robust Machine Learning Algorithms and Systems: Detection & Mitigation of Adversarial Attacks and Anomalies

The National Academies of Sciences, Engineering, and Medicine are private, nonprofit institutions that provide expert advice on some of the most pressing challenges facing the nation and world. Our ...
CSOonline

Adversarial machine learning explained: How attackers disrupt AI and ML systems

Threat actors have several ways to fool or exploit artificial intelligence and machine learning systems and models, but you can defend against their tactics. As more companies roll out artificial ...
InfoWorld

Anti-adversarial machine learning defenses start to take root

Much of the anti-adversarial research has been on the potential for minute, largely undetectable alterations to images (researchers generally refer to these as “noise perturbations”) that cause AI’s ...