Ambra Demontis — accepted AI-conference papers

2025

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples

Antonio Emanuele Cinà, Jérôme Rony, Maura Pintor, Luca Demetrio, Ambra Demontis, Battista Biggio +2

AAAI 2025technical

While novel gradient-based attacks are continuously proposed to improve the optimization of adversarial examples, each is shown to outperform its predecessors using different experimental setups, implementations, and computational budgets, leading to biased and unfair comparisons. In this work, we o…

Cited by 5 Source ↗PDF ↗Scholar ↗

2022

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples

Maura Pintor, Luca Demetrio, Angelo Sotgiu, Ambra Demontis, Nicholas Carlini, Battista Biggio +1

NeurIPS 2022accept

Evaluating robustness of machine-learning models to adversarial examples is a challenging problem. Many defenses have been shown to provide a false sense of robustness by causing gradient-based attacks to fail, and they have been broken under more rigorous evaluations. Although guidelines and best p…

Cited by 44 Source ↗PDF ↗code ↗Scholar ↗