# FastAT Benchmark: A Comprehensive Framework for Fair Evaluation of Fast Adversarial Training Methods

arXiv: https://arxiv.org/abs/2604.22853 · HTML: https://mlsanity.com/paper/2604.22853/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 22 of 22)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Fast is better than free: Revisiting adversarial training](https://mlsanity.com/paper/2001.03994/) (2020)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Understanding and Improving Fast Adversarial Training](https://mlsanity.com/paper/2007.02617/) (2020)
- [Understanding Catastrophic Overfitting in Single-step Adversarial Training](https://mlsanity.com/paper/2010.01799/) (2020)
- [Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses](https://mlsanity.com/paper/2011.14969/) (2020)
- [Robust Principles: Architectural Design Principles for Adversarially Robust CNNs](https://mlsanity.com/paper/2308.16258/) (2023)
- [Make Some Noise: Reliable and Efficient Single-Step Adversarial Training](https://mlsanity.com/paper/2202.01181/) (2022)
- [Adversarial Robustness Limits via Scaling-Law and Human-Alignment Studies](https://mlsanity.com/paper/2404.09349/) (2024)
- [Prior-Guided Adversarial Initialization for Fast Adversarial Training](https://mlsanity.com/paper/2207.08859/) (2022)
- [Fast Adversarial Training with Smooth Convergence](https://mlsanity.com/paper/2308.12857/) (2023)
- [Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective](https://mlsanity.com/paper/2407.12443/) (2024)
- [Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization](https://mlsanity.com/paper/2404.08154/) (2024)
- [Efficient local linearity regularization to overcome catastrophic overfitting](https://mlsanity.com/paper/2401.11618/) (2024)

## Built on by (most cited first, 1 of 1)

- [SORA: Free Second-Order Attacks in Fast Adversarial Training](https://mlsanity.com/paper/2606.00738/) (2026)

## Abstract (arXiv)

Fast Adversarial Training (FastAT) seeks to achieve adversarial robustness at a fraction of the computational cost incurred by standard multi-step methods such as PGD-AT. Although numerous FastAT techniques have been proposed in recent years, fair comparison among them remains elusive. Existing benchmarks and public leaderboards typically permit diverse model architectures, varying training configurations, and external data sources, making it unclear whether reported improvements reflect genuine algorithmic advances or merely more favorable experimental conditions. To address this problem, we introduce the FastAT Benchmark, a controlled evaluation framework built on three core design principles: unified architecture requirements, standardized training settings, and strict prohibition of external or synthetic data. The benchmark implements over twenty representative FastAT methods within a single codebase, enabling direct and reproducible comparison. Each method is assessed through a dual-metric evaluation framework that measures both adversarial robustness (accuracy under PGD, AutoAttack, and CR Attack) and computational cost (GPU training time and peak memory footprint). Comprehensive experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet provide reliable baseline measurements and reveal that well-designed single-step methods can match or surpass PGD-AT robustness at substantially lower cost, while no single method dominates across all evaluation dimensions. The complete benchmark, including source code, configuration files, and experimental results, is publicly available to support transparent and fair evaluation of future FastAT research.