Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization

Autor:	Floris, Giuseppe, Mura, Raffaele, Scionis, Luca, Piras, Giorgio, Pintor, Maura, Demontis, Ambra, Biggio, Battista
Rok vydání:	2023
Předmět:	Computer Science - Machine Learning Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
DOI:	10.14428/esann/2023.ES2023-164
Popis:	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. Comment: Accepted at ESANN23
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2310.08177 Zobrazit plný text záznamu View this record from Arxiv