Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization
arXiv:2310.08177 · doi:10.14428/esann/2023.ES2023-164
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.
Accepted at ESANN23