Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization

• URL: http://arxiv.org/abs/2310.08177v1
• Date: Thu, 12 Oct 2023 10:03:25 GMT
• Title: Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization
• Authors: Giuseppe Floris, Raffaele Mura, Luca Scionis, Giorgio Piras, Maura Pintor, Ambra Demontis, Battista Biggio
• Abstract summary: We show that hyperparameter optimization can improve fast minimum-norm attacks by the selection of the loss function, the automation and the step-size scheduler. We release our open-source code at https://www.pralab.com/HO-FMN.
• Score: 12.526318578195724
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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