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…