# Fast and Reliable Evaluation of Adversarial Robustness with Minimum-Margin Attack

arXiv: https://arxiv.org/abs/2206.07314 · HTML: https://mlsanity.com/paper/2206.07314/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 54 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Wide Residual Networks](https://mlsanity.com/paper/1605.07146/) (2016)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [Theoretically Principled Trade-off between Robustness and Accuracy](https://mlsanity.com/paper/1901.08573/) (2019)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Fast is better than free: Revisiting adversarial training](https://mlsanity.com/paper/2001.03994/) (2020)
- [Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods](https://mlsanity.com/paper/1705.07263/) (2017)
- [Overfitting in adversarially robust deep learning](https://mlsanity.com/paper/2002.11569/) (2020)
- [Unlabeled Data Improves Adversarial Robustness](https://mlsanity.com/paper/1905.13736/) (2019)
- [Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples](https://mlsanity.com/paper/2010.03593/) (2020)
- [Attacks Which Do Not Kill Training Make Adversarial Learning Stronger](https://mlsanity.com/paper/2002.11242/) (2020)
- [Understanding and Improving Fast Adversarial Training](https://mlsanity.com/paper/2007.02617/) (2020)
- [Fixing Data Augmentation to Improve Adversarial Robustness](https://mlsanity.com/paper/2103.01946/) (2021)
- [Geometry-aware Instance-reweighted Adversarial Training](https://mlsanity.com/paper/2010.01736/) (2020)
- [On the Convergence and Robustness of Adversarial Training](https://mlsanity.com/paper/2112.08304/) (2021)
- [Adversarial Robustness on In- and Out-Distribution Improves Explainability](https://mlsanity.com/paper/2003.09461/) (2020)
- [Quantum Entanglement in Deep Learning Architectures](https://mlsanity.com/paper/1803.09780/) (2018)
- [An Alternative Surrogate Loss for PGD-based Adversarial Testing](https://mlsanity.com/paper/1910.09338/) (2019)
- [Maximum Mean Discrepancy Test is Aware of Adversarial Attacks](https://mlsanity.com/paper/2010.11415/) (2020)
- [Globally Normalized Transition-Based Neural Networks](https://mlsanity.com/paper/1603.06042/) (2016)

## Built on by (most cited first, 13 of 13)

- [Detecting Adversarial Data by Probing Multiple Perturbations Using Expected Perturbation Score](https://mlsanity.com/paper/2305.16035/) (2023)
- [Towards Lightweight Black-Box Attacks against Deep Neural Networks](https://mlsanity.com/paper/2209.14826/) (2022)
- [HATS: Hardness-Aware Trajectory Synthesis for GUI Agents](https://mlsanity.com/paper/2603.12138/) (2026)
- [Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training](https://mlsanity.com/paper/2406.00685/) (2024)
- [Segment-Anything Models Achieve Zero-shot Robustness in Autonomous Driving](https://mlsanity.com/paper/2408.09839/) (2024)
- [OODRobustBench: a Benchmark and Large-Scale Analysis of Adversarial Robustness under Distribution Shift](https://mlsanity.com/paper/2310.12793/) (2023)
- [One Stone, Two Birds: Enhancing Adversarial Defense Through the Lens of Distributional Discrepancy](https://mlsanity.com/paper/2503.02169/) (2025)
- [Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains](https://mlsanity.com/paper/2505.19397/) (2025)
- [Detecting Adversarial Data using Perturbation Forgery](https://mlsanity.com/paper/2405.16226/) (2024)
- [Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets](https://mlsanity.com/paper/2306.15482/) (2023)
- [Sample-wise Adaptive Weighting for Transfer Consistency in Adversarial Distillation](https://mlsanity.com/paper/2512.10275/) (2025)
- [A Unified Perspective on Adversarial Membership Manipulation in Vision Models](https://mlsanity.com/paper/2604.02780/) (2026)
- [Margin-Drop Coordinates for Cross-Budget Robustness Evaluation](https://mlsanity.com/paper/2609.26081/) (2026)

## Abstract (arXiv)

The AutoAttack (AA) has been the most reliable method to evaluate adversarial robustness when considerable computational resources are available. However, the high computational cost (e.g., 100 times more than that of the project gradient descent attack) makes AA infeasible for practitioners with limited computational resources, and also hinders applications of AA in the adversarial training (AT). In this paper, we propose a novel method, minimum-margin (MM) attack, to fast and reliably evaluate adversarial robustness. Compared with AA, our method achieves comparable performance but only costs 3% of the computational time in extensive experiments. The reliability of our method lies in that we evaluate the quality of adversarial examples using the margin between two targets that can precisely identify the most adversarial example. The computational efficiency of our method lies in an effective Sequential TArget Ranking Selection (STARS) method, ensuring that the cost of the MM attack is independent of the number of classes. The MM attack opens a new way for evaluating adversarial robustness and provides a feasible and reliable way to generate high-quality adversarial examples in AT.