# Torchattacks: A PyTorch Repository for Adversarial Attacks

arXiv: https://arxiv.org/abs/2010.01950 · HTML: https://mlsanity.com/paper/2010.01950/

## Key facts

- Cited by 158 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2020.

## Built on by (most cited first, 30 of 158)

- [On the Robustness of Large Multimodal Models Against Image Adversarial Attacks](https://mlsanity.com/paper/2312.03777/) (2023)
- [AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors](https://mlsanity.com/paper/2310.17419/) (2023)
- [LyaNet: A Lyapunov Framework for Training Neural ODEs](https://mlsanity.com/paper/2202.02526/) (2022)
- [Data Augmentation Alone Can Improve Adversarial Training](https://mlsanity.com/paper/2301.09879/) (2023)
- [Elucidating the Design Space of Dataset Condensation](https://mlsanity.com/paper/2404.13733/) (2024)
- [BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning](https://mlsanity.com/paper/2305.04769/) (2023)
- [On the Duality Between Sharpness-Aware Minimization and Adversarial Training](https://mlsanity.com/paper/2402.15152/) (2024)
- [Out-Of-Distribution Detection Is Not All You Need](https://mlsanity.com/paper/2211.16158/) (2022)
- [TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Models](https://mlsanity.com/paper/2411.13136/) (2024)
- [You Only Cut Once: Boosting Data Augmentation with a Single Cut](https://mlsanity.com/paper/2201.12078/) (2022)
- [Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularization](https://mlsanity.com/paper/2212.04985/) (2022)
- [FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Models](https://mlsanity.com/paper/2509.19870/) (2025)
- [Pixle: a fast and effective black-box attack based on rearranging pixels](https://mlsanity.com/paper/2202.02236/) (2022)
- [LLM-Safety Evaluations Lack Robustness](https://mlsanity.com/paper/2503.02574/) (2025)
- [k-Mixup Regularization for Deep Learning via Optimal Transport](https://mlsanity.com/paper/2106.02933/) (2021)
- [Differentially Private Sharpness-Aware Training](https://mlsanity.com/paper/2306.05651/) (2023)
- [AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples](https://mlsanity.com/paper/2404.19460/) (2024)
- [Adversarial Visual Robustness by Causal Intervention](https://mlsanity.com/paper/2106.09534/) (2021)
- [Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Stream](https://mlsanity.com/paper/2411.05712/) (2024)
- [Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistency](https://mlsanity.com/paper/2108.09891/) (2021)
- [Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks](https://mlsanity.com/paper/2101.06507/) (2021)
- [Projected Randomized Smoothing for Certified Adversarial Robustness](https://mlsanity.com/paper/2309.13794/) (2023)
- [Improving Adversarial Transferability via Model Alignment](https://mlsanity.com/paper/2311.18495/) (2023)
- [Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imaging](https://mlsanity.com/paper/2411.04376/) (2024)
- [Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithms](https://mlsanity.com/paper/2108.13446/) (2021)
- [Exploring the Effect of Multi-step Ascent in Sharpness-Aware Minimization](https://mlsanity.com/paper/2302.10181/) (2023)
- [DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection](https://mlsanity.com/paper/2306.01272/) (2023)
- [Well-classified Examples are Underestimated in Classification with Deep Neural Networks](https://mlsanity.com/paper/2110.06537/) (2021)
- [Understanding the effect of sparsity on neural networks robustness](https://mlsanity.com/paper/2206.10915/) (2022)
- [Adversarial Purification with the Manifold Hypothesis](https://mlsanity.com/paper/2210.14404/) (2022)

## Abstract (arXiv)

Torchattacks is a PyTorch library that contains adversarial attacks to generate adversarial examples and to verify the robustness of deep learning models. The code can be found at https://github.com/Harry24k/adversarial-attacks-pytorch.