428 citations · 763 across the 12 of their papers we have counts for
9 papers · 1 filter
Adversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluation
Luca Scionis, Luca Melis, Maura Pintor +5
Adversarial robustness is commonly evaluated with predefined attack ensembles, such as AutoAttack, at a single perturbation budget and on a selective choice of pertur…
On the Robustness of Adversarial Training Against Uncertainty Attacks
Emanuele Ledda, Giovanni Scodeller, Daniele Angioni +5
In learning problems, the noise inherent to the task at hand hinders the possibility to infer without a certain degree of uncertainty. Quantifying this uncertainty, regardless of i…
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks
Emanuele Ledda, Giorgio Fumera, Fabio Roli
Among Bayesian methods, Monte-Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks. Its popularity recently led to seminal works that…
Is feature selection secure against training data poisoning?
Huang Xiao, Battista Biggio, Gavin Brown +3
Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation o…
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…
Security Evaluation of Support Vector Machines in Adversarial Environments
Battista Biggio, Igino Corona, Blaine Nelson +5
Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering…