15 citations · 28 across the 11 of their papers we have counts for
3 papers · 1 filter
A Minimax Approach Against Multi-Armed Adversarial Attacks Detection
Federica Granese, Marco Romanelli, Siddharth Garg +1
Multi-armed adversarial attacks, in which multiple algorithms and objective loss functions are simultaneously used at evaluation time, have been shown to be highly successful in fo…
MEAD: A Multi-Armed Approach for Evaluation of Adversarial Examples Detectors
Federica Granese, Marine Picot, Marco Romanelli +2
Detection of adversarial examples has been a hot topic in the last years due to its importance for safely deploying machine learning algorithms in critical applications. However, t…
DOCTOR: A Simple Method for Detecting Misclassification Errors
Federica Granese, Marco Romanelli, Daniele Gorla +2
Deep neural networks (DNNs) have shown to perform very well on large scale object recognition problems and lead to widespread use for real-world applications, including situations…