10 citations · 12 across the 5 of their papers we have counts for
5 papers
Optimal Zero-Shot Detector for Multi-Armed Attacks
Federica Granese, Marco Romanelli, Pablo Piantanida
This paper explores a scenario in which a malicious actor employs a multi-armed attack strategy to manipulate data samples, offering them various avenues to introduce noise into th…
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…
On the (Im)Possibility of Estimating Various Notions of Differential Privacy
Daniele Gorla, Louis Jalouzot, Federica Granese +2
We analyze to what extent final users can infer information about the level of protection of their data when the data obfuscation mechanism is a priori unknown to them (the so-call…
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…