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Maura Pintor

5 accepted papers

2025

$\sigma$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples

ICLR 2025poster

Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging. While most attacks consider $\ell_2$- and $\ell_\infty$-norm constraints to craft input perturbations, only a few investigate sparse $\ell_1$- and $\ell_0$-norm attacks. In particular, $\ell_0$-norm atta…

Cited by 0SourcePDFScholar
2025

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples

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 5SourcePDFScholar
2025

TransferBench: Benchmarking Ensemble-based Black-box Transfer Attacks

NeurIPS 2025poster

Ensemble-based black-box transfer attacks optimize adversarial examples on a set of surrogate models, claiming to reach high success rates by querying the (unknown) target model only a few times. In this work, we show that prior evaluations are systematically biased, as such methods are tested only…

Cited by 0SourcecodeScholar
2022

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

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…

2021

Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints

NeurIPS 2021poster

Evaluating adversarial robustness amounts to finding the minimum perturbation needed to have an input sample misclassified. The inherent complexity of the underlying optimization requires current gradient-based attacks to be carefully tuned, initialized, and possibly executed for many computational…