# When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture

arXiv: https://arxiv.org/abs/2210.07540 · HTML: https://mlsanity.com/paper/2210.07540/

## Key facts

- Cited by 64 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 54 references that are in the map.
- First posted on arXiv 14 Oct 2022.

## Builds on (most cited first, 30 of 35)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (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)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [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)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples](https://mlsanity.com/paper/1802.00420/) (2018)
- [Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://mlsanity.com/paper/1707.02968/) (2017)
- [Square Attack: a query-efficient black-box adversarial attack via random search](https://mlsanity.com/paper/1912.00049/) (2019)
- [RobustBench: a standardized adversarial robustness benchmark](https://mlsanity.com/paper/2010.09670/) (2020)
- [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://mlsanity.com/paper/2106.10270/) (2021)
- [Overfitting in adversarially robust deep learning](https://mlsanity.com/paper/2002.11569/) (2020)
- [ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases](https://mlsanity.com/paper/2103.10697/) (2021)
- [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)
- [Bag of Tricks for Adversarial Training](https://mlsanity.com/paper/2010.00467/) (2020)
- [Are Transformers More Robust Than CNNs?](https://mlsanity.com/paper/2111.05464/) (2021)
- [On the Robustness of Vision Transformers to Adversarial Examples](https://mlsanity.com/paper/2104.02610/) (2021)
- [TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up](https://mlsanity.com/paper/2102.07074/) (2021)

## Built on by (most cited first, 30 of 64)

- [Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models](https://mlsanity.com/paper/2303.01870/) (2023)
- [Fight Back Against Jailbreaking via Prompt Adversarial Tuning](https://mlsanity.com/paper/2402.06255/) (2024)
- [CFA: Class-wise Calibrated Fair Adversarial Training](https://mlsanity.com/paper/2303.14460/) (2023)
- [Robust Principles: Architectural Design Principles for Adversarially Robust CNNs](https://mlsanity.com/paper/2308.16258/) (2023)
- [Revisiting Adversarial Training at Scale](https://mlsanity.com/paper/2401.04727/) (2024)
- [TERD: A Unified Framework for Safeguarding Diffusion Models Against Backdoors](https://mlsanity.com/paper/2409.05294/) (2024)
- [On the Duality Between Sharpness-Aware Minimization and Adversarial Training](https://mlsanity.com/paper/2402.15152/) (2024)
- [Language-Driven Anchors for Zero-Shot Adversarial Robustness](https://mlsanity.com/paper/2301.13096/) (2023)
- [Balance, Imbalance, and Rebalance: Understanding Robust Overfitting from a Minimax Game Perspective](https://mlsanity.com/paper/2310.19360/) (2023)
- [One Surrogate to Fool Them All: Universal, Transferable, and Targeted Adversarial Attacks with CLIP](https://mlsanity.com/paper/2505.19840/) (2025)
- [Average of Pruning: Improving Performance and Stability of Out-of-Distribution Detection](https://mlsanity.com/paper/2303.01201/) (2023)
- [Generalist: Decoupling Natural and Robust Generalization](https://mlsanity.com/paper/2303.13813/) (2023)
- [Sharpness-Aware Minimization Alone can Improve Adversarial Robustness](https://mlsanity.com/paper/2305.05392/) (2023)
- [On the Importance of Backbone to the Adversarial Robustness of Object Detectors](https://mlsanity.com/paper/2305.17438/) (2023)
- [MIMIR: Masked Image Modeling for Mutual Information-based Adversarial Robustness](https://mlsanity.com/paper/2312.04960/) (2023)
- [E2AT: Multimodal Jailbreak Defense via Dynamic Joint Optimization for Multimodal Large Language Models](https://mlsanity.com/paper/2503.04833/) (2025)
- [FullLoRA: Efficiently Boosting the Robustness of Pretrained Vision Transformers](https://mlsanity.com/paper/2401.01752/) (2024)
- [Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense](https://mlsanity.com/paper/2302.01056/) (2023)
- [σ-zero: Gradient-based Optimization of ℓ0-norm Adversarial Examples](https://mlsanity.com/paper/2402.01879/) (2024)
- [RobArch: Designing Robust Architectures against Adversarial Attacks](https://mlsanity.com/paper/2301.03110/) (2023)
- [AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation](https://mlsanity.com/paper/2306.07197/) (2023)
- [PID: Prompt-Independent Data Protection Against Latent Diffusion Models](https://mlsanity.com/paper/2406.15305/) (2024)
- [Adversarial Training: A Survey](https://mlsanity.com/paper/2410.15042/) (2024)
- [SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization](https://mlsanity.com/paper/2402.03317/) (2024)
- [Revisiting the Trade-off between Accuracy and Robustness via Weight Distribution of Filters](https://mlsanity.com/paper/2306.03430/) (2023)
- [Catastrophic Overfitting: A Potential Blessing in Disguise](https://mlsanity.com/paper/2402.18211/) (2024)
- [Watch the Watcher! Backdoor Attacks on Security-Enhancing Diffusion Models](https://mlsanity.com/paper/2406.09669/) (2024)
- [Mitigating Low-Frequency Bias: Feature Recalibration and Frequency Attention Regularization for Adversarial Robustness](https://mlsanity.com/paper/2407.04016/) (2024)
- [SAFER: Sharpness Aware layer-selective Finetuning for Enhanced Robustness in vision transformers](https://mlsanity.com/paper/2501.01529/) (2025)
- [Boosting Generative Adversarial Transferability with Self-supervised Vision Transformer Features](https://mlsanity.com/paper/2506.21046/) (2025)

## Abstract (arXiv)

Vision Transformers (ViTs) have recently achieved competitive performance in broad vision tasks. Unfortunately, on popular threat models, naturally trained ViTs are shown to provide no more adversarial robustness than convolutional neural networks (CNNs). Adversarial training is still required for ViTs to defend against such adversarial attacks. In this paper, we provide the first and comprehensive study on the adversarial training recipe of ViTs via extensive evaluation of various training techniques across benchmark datasets. We find that pre-training and SGD optimizer are necessary for ViTs' adversarial training. Further considering ViT as a new type of model architecture, we investigate its adversarial robustness from the perspective of its unique architectural components. We find, when randomly masking gradients from some attention blocks or masking perturbations on some patches during adversarial training, the adversarial robustness of ViTs can be remarkably improved, which may potentially open up a line of work to explore the architectural information inside the newly designed models like ViTs. Our code is available at https://github.com/mo666666/When-Adversarial-Training-Meets-Vision-Transformers.