Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization
Giuseppe Floris Raffaele Mura Luca Scionis Giorgio Piras Maura Pintor Ambra Demontis Battista Biggio
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.