Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization
Machine Learning
2023-10-13 v1 Computer Vision and Pattern Recognition
Abstract
Evaluating the adversarial robustness of machine learning models using gradient-based attacks is challenging. In this work, we show that hyperparameter optimization can improve fast minimum-norm attacks by automating the selection of the loss function, the optimizer and the step-size scheduler, along with the corresponding hyperparameters. Our extensive evaluation involving several robust models demonstrates the improved efficacy of fast minimum-norm attacks when hyper-up with hyperparameter optimization. We release our open-source code at https://github.com/pralab/HO-FMN.
Cite
@article{arxiv.2310.08177,
title = {Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization},
author = {Giuseppe Floris and Raffaele Mura and Luca Scionis and Giorgio Piras and Maura Pintor and Ambra Demontis and Battista Biggio},
journal= {arXiv preprint arXiv:2310.08177},
year = {2023}
}
Comments
Accepted at ESANN23