# Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization

arXiv: https://arxiv.org/abs/2310.08177 · HTML: https://mlsanity.com/paper/2310.08177/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 16 references that are in the map.
- First posted on arXiv 12 Oct 2023.

## Builds on (most cited first, 14 of 14)

- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Improving Robustness using Generated Data](https://mlsanity.com/paper/2110.09468/) (2021)
- [Better Diffusion Models Further Improve Adversarial Training](https://mlsanity.com/paper/2302.04638/) (2023)
- [Fixing Data Augmentation to Improve Adversarial Robustness](https://mlsanity.com/paper/2103.01946/) (2021)
- [A System for Massively Parallel Hyperparameter Tuning](https://mlsanity.com/paper/1810.05934/) (2018)
- [Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?](https://mlsanity.com/paper/2104.09425/) (2021)
- [Robustness and Accuracy Could Be Reconcilable by (Proper) Definition](https://mlsanity.com/paper/2202.10103/) (2022)
- [Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints](https://mlsanity.com/paper/2102.12827/) (2021)
- [Automated Discovery of Adaptive Attacks on Adversarial Defenses](https://mlsanity.com/paper/2102.11860/) (2021)

## Built on by (most cited first, 4 of 4)

- [Natural Language Induced Adversarial Images](https://mlsanity.com/paper/2410.08620/) (2024)
- [Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent](https://mlsanity.com/paper/2503.19347/) (2025)
- [Adversarial Machine Learning Attacks on Financial Reporting via Maximum Violated Multi-Objective Attack](https://mlsanity.com/paper/2507.05441/) (2025)
- [HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks](https://mlsanity.com/paper/2407.08806/) (2024)

## Abstract (arXiv)

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.