3 papers
cs.LG2020
FADER: Fast Adversarial Example Rejection
Francesco Crecchi, Marco Melis, Angelo Sotgiu +2
Deep neural networks are vulnerable to adversarial examples, i.e., carefully-crafted inputs that mislead classification at test time. Recent defenses have been shown to improve adv…
cs.CV2019
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…
cs.CR2018
IntelliAV: Building an Effective On-Device Android Malware Detector
Mansour Ahmadi, Angelo Sotgiu, Giorgio Giacinto
The importance of employing machine learning for malware detection has become explicit to the security community. Several anti-malware vendors have claimed and advertised the appli…