Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates

no code implementations • 27 Feb 2024 • Daniele Angioni, Luca Demetrio, Maura Pintor, Luca Oneto, Davide Anguita, Battista Biggio, Fabio Roli

In this work, we show that this problem also affects robustness to adversarial examples, thereby hindering the development of secure model update practices.

Adversarial Robustness regression

Adversarial Attacks Against Uncertainty Quantification

no code implementations • 19 Sep 2023 • Emanuele Ledda, Daniele Angioni, Giorgio Piras, Giorgio Fumera, Battista Biggio, Fabio Roli

Machine-learning models can be fooled by adversarial examples, i. e., carefully-crafted input perturbations that force models to output wrong predictions.

Semantic Segmentation Uncertainty Quantification

ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches

1 code implementation • 7 Mar 2022 • Maura Pintor, Daniele Angioni, Angelo Sotgiu, Luca Demetrio, Ambra Demontis, Battista Biggio, Fabio Roli

We showcase the usefulness of this dataset by testing the effectiveness of the computed patches against 127 models.

Adversarial Robustness Benchmarking +1

Are spoofs from latent fingerprints a real threat for the best state-of-art liveness detectors?

no code implementations • 7 Jul 2020 • Roberto Casula, Giulia Orrù, Daniele Angioni, Xiaoyi Feng, Gian Luca Marcialis, Fabio Roli

We investigated the threat level of realistic attacks using latent fingerprints against sensors equipped with state-of-art liveness detectors and fingerprint verification systems which integrate such liveness algorithms.