Our new paper MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors, which sets up a new evaluation framework for adversarial example detection mechanisms, has been accepted to appear at ECMLPKDD 2022.
Marco Romanelli
Research Associate
My research interests include applications of Information Theory notions to Privacy and Security, Safety in AI, Machine Learning and Information Leakage Measurement.