# Improving Fast Adversarial Training with Prior-Guided Knowledge

arXiv: https://arxiv.org/abs/2304.00202 · HTML: https://mlsanity.com/paper/2304.00202/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- Builds on 53 of its 76 references that are in the map.
- First posted on arXiv 1 Apr 2023.

## Builds on (most cited first, 30 of 53)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (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)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (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)
- [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)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Averaging Weights Leads to Wider Optima and Better Generalization](https://mlsanity.com/paper/1803.05407/) (2018)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Fast is better than free: Revisiting adversarial training](https://mlsanity.com/paper/2001.03994/) (2020)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [Adversarial Training for Free!](https://mlsanity.com/paper/1904.12843/) (2019)
- [Cyclical Learning Rates for Training Neural Networks](https://mlsanity.com/paper/1506.01186/) (2015)
- [Overfitting in adversarially robust deep learning](https://mlsanity.com/paper/2002.11569/) (2020)
- [Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack](https://mlsanity.com/paper/1907.02044/) (2019)
- [Uncovering the Limits of Adversarial Training against Norm-Bounded Adversarial Examples](https://mlsanity.com/paper/2010.03593/) (2020)
- [PixelDefend: Leveraging Generative Models to Understand and Defend against Adversarial Examples](https://mlsanity.com/paper/1710.10766/) (2017)
- [Improving Robustness using Generated Data](https://mlsanity.com/paper/2110.09468/) (2021)
- [Adversarial Logit Pairing](https://mlsanity.com/paper/1803.06373/) (2018)
- [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)
- [Bridging Mode Connectivity in Loss Landscapes and Adversarial Robustness](https://mlsanity.com/paper/2005.00060/) (2020)
- [Robustness and Accuracy Could Be Reconcilable by (Proper) Definition](https://mlsanity.com/paper/2202.10103/) (2022)
- [ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples](https://mlsanity.com/paper/1811.12673/) (2018)
- [Boosting Adversarial Training with Hypersphere Embedding](https://mlsanity.com/paper/2002.08619/) (2020)

## Built on by (most cited first, 30 of 35)

- [Perception-guided Jailbreak against Text-to-Image Models](https://mlsanity.com/paper/2408.10848/) (2024)
- [Efficient Generation of Targeted and Transferable Adversarial Examples for Vision-Language Models Via Diffusion Models](https://mlsanity.com/paper/2404.10335/) (2024)
- [OT-Attack: Enhancing Adversarial Transferability of Vision-Language Models via Optimal Transport Optimization](https://mlsanity.com/paper/2312.04403/) (2023)
- [Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial Trajectory](https://mlsanity.com/paper/2403.12445/) (2024)
- [Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for Multimodal Large Language Models](https://mlsanity.com/paper/2412.05934/) (2024)
- [Towards Viewpoint-Invariant Visual Recognition via Adversarial Training](https://mlsanity.com/paper/2307.10235/) (2023)
- [Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language Attack](https://mlsanity.com/paper/2411.02669/) (2024)
- [Object Detectors in the Open Environment: Challenges, Solutions, and Outlook](https://mlsanity.com/paper/2403.16271/) (2024)
- [Texture Re-scalable Universal Adversarial Perturbation](https://mlsanity.com/paper/2406.06089/) (2024)
- [Fast Adversarial Training with Weak-to-Strong Spatial-Temporal Consistency in the Frequency Domain on Videos](https://mlsanity.com/paper/2504.14921/) (2025)
- [Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation](https://mlsanity.com/paper/2312.05508/) (2023)
- [AdPO: Enhancing the Adversarial Robustness of Large Vision-Language Models with Preference Optimization](https://mlsanity.com/paper/2504.01735/) (2025)
- [Robust Overfitting Does Matter: Test-Time Adversarial Purification With FGSM](https://mlsanity.com/paper/2403.11448/) (2024)
- [Adversarial Training: A Survey](https://mlsanity.com/paper/2410.15042/) (2024)
- [Enhancing Targeted Adversarial Attacks on Large Vision-Language Models via Intermediate Projector](https://mlsanity.com/paper/2508.13739/) (2025)
- [Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filters](https://mlsanity.com/paper/2306.03430/) (2023)
- [Improving Fast Adversarial Training via Self-Knowledge Guidance](https://mlsanity.com/paper/2409.17589/) (2024)
- [S4ST: A Strong, Self-transferable, faSt, and Simple Scale Transformation for Transferable Targeted Attack](https://mlsanity.com/paper/2410.13891/) (2024)
- [Conflict-Aware Adversarial Training](https://mlsanity.com/paper/2410.16579/) (2024)
- [Light as Deception: GPT-driven Natural Relighting Against Vision-Language Pre-training Models](https://mlsanity.com/paper/2505.24227/) (2025)
- [AdaGAT: Adaptive Guidance Adversarial Training for the Robustness of Deep Neural Networks](https://mlsanity.com/paper/2508.17265/) (2025)
- [CIARD: Cyclic Iterative Adversarial Robustness Distillation](https://mlsanity.com/paper/2509.12633/) (2025)
- [Parameter Interpolation Adversarial Training for Robust Image Classification](https://mlsanity.com/paper/2511.00836/) (2025)
- [Mitigating Error Amplification in Fast Adversarial Training](https://mlsanity.com/paper/2604.24332/) (2026)
- [Enhancing Transferability of Targeted Adversarial Examples: A Self-Universal Perspective](https://mlsanity.com/paper/2407.15683/) (2024)
- [Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective](https://mlsanity.com/paper/2408.03944/) (2024)
- [Feedback-based Modal Mutual Search for Attacking Vision-Language Pre-training Models](https://mlsanity.com/paper/2409.06726/) (2024)
- [Seeking Flat Minima over Diverse Surrogates for Improved Adversarial Transferability: A Theoretical Framework and Algorithmic Instantiation](https://mlsanity.com/paper/2504.16474/) (2025)
- [Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label Distillation](https://mlsanity.com/paper/2506.08611/) (2025)
- [FERD: Fairness-Enhanced Data-Free Robustness Distillation](https://mlsanity.com/paper/2509.20793/) (2025)

## Abstract (arXiv)

Fast adversarial training (FAT) is an efficient method to improve robustness. However, the original FAT suffers from catastrophic overfitting, which dramatically and suddenly reduces robustness after a few training epochs. Although various FAT variants have been proposed to prevent overfitting, they require high training costs. In this paper, we investigate the relationship between adversarial example quality and catastrophic overfitting by comparing the training processes of standard adversarial training and FAT. We find that catastrophic overfitting occurs when the attack success rate of adversarial examples becomes worse. Based on this observation, we propose a positive prior-guided adversarial initialization to prevent overfitting by improving adversarial example quality without extra training costs. This initialization is generated by using high-quality adversarial perturbations from the historical training process. We provide theoretical analysis for the proposed initialization and propose a prior-guided regularization method that boosts the smoothness of the loss function. Additionally, we design a prior-guided ensemble FAT method that averages the different model weights of historical models using different decay rates. Our proposed method, called FGSM-PGK, assembles the prior-guided knowledge, i.e., the prior-guided initialization and model weights, acquired during the historical training process. Evaluations of four datasets demonstrate the superiority of the proposed method.