Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization

2023-10-12 · 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.

📄 PDF Abstract BibTeX arXiv:2310.08177

Code (1)

pralab/HO-FMN 공식 구현 pytorch

Tasks

Adversarial Robustness Hyperparameter Optimization

HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks

2024-07-11 · Raffaele Mura, Giuseppe Floris, Luca Scionis, Giorgio Piras 외

Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models. However, many attacks tend to provide overly-optimistic evaluations as they use fixed loss functions, optimizers, step-size sch…

Adversarial Robustness Hyperparameter Optimization

Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints

2021-02-25 · NeurIPS 2021 12 · Maura Pintor, Fabio Roli, Wieland Brendel, Battista Biggio

Evaluating adversarial robustness amounts to finding the minimum perturbation needed to have an input sample misclassified. The inherent complexity of the underlying optimization requires current gradient-based attacks t…

Adversarial Attack Adversarial Robustness

$σ$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples

2024-02-02 · Antonio Emanuele Cinà, Francesco Villani, Maura Pintor, Lea Schönherr 외

Evaluating the adversarial robustness of deep networks to gradient-based attacks is challenging. While most attacks consider $\ell_2$- and $\ell_\infty$-norm constraints to craft input perturbations, only a few investiga…

Adversarial Robustness

Characterizing Implicit Bias in Terms of Optimization Geometry

2018-02-22 · ICML 2018 7 · Suriya Gunasekar, Jason Lee, Daniel Soudry, Nathan Srebro

We study the implicit bias of generic optimization methods, such as mirror descent, natural gradient descent, and steepest descent with respect to different potentials and norms, when optimizing underdetermined linear re…

General Classification regression

AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient

2022-10-13 · Farzad Nikfam, Alberto Marchisio, Maurizio Martina, Muhammad Shafique

Adversarial training is exploited to develop a robust Deep Neural Network (DNN) model against the malicious altered data. These attacks may have catastrophic effects on DNN models but are indistinguishable for a human be…

Adversarial Attack