428 citations · 763 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
Security Evaluation of Pattern Classifiers under Attack
Battista Biggio, Giorgio Fumera, Fabio Roli
Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be p…
On Security and Sparsity of Linear Classifiers for Adversarial Settings
Ambra Demontis, Paolo Russu, Battista Biggio +2
Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to a…
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
Marco Melis, Ambra Demontis, Battista Biggio +3
Deep neural networks have been widely adopted in recent years, exhibiting impressive performances in several application domains. It has however been shown that they can be fooled…
Progressive Boosting for Class Imbalance
Roghayeh Soleymani, Eric Granger, Giorgio Fumera
Pattern recognition applications often suffer from skewed data distributions between classes, which may vary during operations w.r.t. the design data. Two-class classification syst…