428 citations · 763 across the 12 of their papers we have counts for
5 papers · 1 filter
Adversarial Attacks Against Uncertainty Quantification
Emanuele Ledda, Daniele Angioni, Giorgio Piras +3
Machine-learning models can be fooled by adversarial examples, i.e., carefully-crafted input perturbations that force models to output wrong predictions. While uncertainty quantifi…
Deep Neural Rejection against Adversarial Examples
Angelo Sotgiu, Ambra Demontis, Marco Melis +4
Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples t…
Super-sparse Learning in Similarity Spaces
Ambra Demontis, Marco Melis, Battista Biggio +2
In several applications, input samples are more naturally represented in terms of similarities between each other, rather than in terms of feature vectors. In these settings, machi…
Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems
Battista Biggio, Giorgio Fumera, Gian Luca Marcialis +1
Prior work has shown that multibiometric systems are vulnerable to presentation attacks, assuming that their matching score distribution is identical to that of genuine users, with…
A Multiple Component Matching Framework for Person Re-Identification
Riccardo Satta, Giorgio Fumera, Fabio Roli +2
Person re-identification consists in recognizing an individual that has already been observed over a network of cameras. It is a novel and challenging research topic in computer vi…