15 citations · 28 across the 11 of their papers we have counts for
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cs.CV2022
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…
cs.LG2022★ 15 cited
Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning
Georg Pichler, Marco Romanelli, Leonardo Rey Vega +1
Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data is ever exchanged either between the clie…