Work

Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization

Year: 2023

Type: conference-paper

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 hyped up with hyperparameter optimization.We release our open-source code at https://github.com/pralab/HO-FMN.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2023.es2023-164