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09/19/2023
Adversarial Attacks Against Uncertainty Quantification
Machine-learning models can be fooled by adversarial examples, i.e., car...
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02/06/2023
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks
Among Bayesian methods, Monte-Carlo dropout provides principled tools fo...
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10/01/2019
Deep Neural Rejection against Adversarial Examples
Despite the impressive performances reported by deep neural networks in ...
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04/21/2018
Is feature selection secure against training data poisoning?
Learning in adversarial settings is becoming an important task for appli...
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12/17/2017
Super-sparse Learning in Similarity Spaces
In several applications, input samples are more naturally represented in...
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08/23/2017
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
Deep neural networks have been widely adopted in recent years, exhibitin...
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06/05/2017
Progressive Boosting for Class Imbalance
Pattern recognition applications often suffer from skewed data distribut...
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09/06/2016
Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems
Prior work has shown that multibiometric systems are vulnerable to prese...
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05/12/2011
