HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks
<|reference_start|>HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks: Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models. However, many attacks tend to provide overly-optimistic evaluations as they use fixed loss functions, optimizers, step-size schedulers, and default hyperparameters. In this work, we tackle these limitations by proposing a parametric variation of the well-known fast minimum-norm attack algorithm, whose loss, optimizer, step-size scheduler, and hyperparameters can be dynamically adjusted. We re-evaluate 12 robust models, showing that our attack finds smaller adversarial perturbations without requiring any additional tuning. This also enables reporting adversarial robustness as a function of the perturbation budget, providing a more complete evaluation than that offered by fixed-budget attacks, while remaining efficient. We release our open-source code at https://github.com/pralab/HO-FMN.<|reference_end|>
@article{mura2024ho-fmn:,
    title={HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks},
    author={Raffaele Mura, Giuseppe Floris, Luca Scionis, Giorgio Piras, Maura
  Pintor, Ambra Demontis, Giorgio Giacinto, Battista Biggio, Fabio Roli},
    journal={arXiv preprint arXiv:2407.08806},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.08806},
    primaryClass={cs.LG}
}