mlsanityData to 7 Oct 2026

Torchattacks: A PyTorch Repository for Adversarial Attacks

arXiv 2010.01950 ↗First posted 24 Sep 2020cs.LG cs.AI cs.CRcited by 156

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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.

Key facts. Cited by 156 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Sep 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 14; 2022: 26; 2023: 41; 2024: 36; 2025: 24; 2026: 15.

156 papers.

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  1. 2023On the Robustness of Large Multimodal Models Against Image Adversarial Attackscited by 89
  2. 2023AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectorscited by 64
  3. 2022LyaNet: A Lyapunov Framework for Training Neural ODEscited by 43
  4. 2023Data Augmentation Alone Can Improve Adversarial Trainingcited by 43
  5. 2024Elucidating the Design Space of Dataset Condensationcited by 40
  6. 2023BiRT: Bio-inspired Replay in Vision Transformers for Continual Learningcited by 25
  7. 2024On the Duality Between Sharpness-Aware Minimization and Adversarial Trainingcited by 24
  8. 2022Out-Of-Distribution Detection Is Not All You Needcited by 23
  9. 2022You Only Cut Once: Boosting Data Augmentation with a Single Cutcited by 22
  10. 2022Understanding and Combating Robust Overfitting via Input Loss Landscape Analysis and Regularizationcited by 22
  11. 2024TAPT: Test-Time Adversarial Prompt Tuning for Robust Inference in Vision-Language Modelscited by 22
  12. 2022Pixle: a fast and effective black-box attack based on rearranging pixelscited by 20
38 more
  1. 2025FreezeVLA: Action-Freezing Attacks against Vision-Language-Action Modelscited by 19
  2. 2025LLM-Safety Evaluations Lack Robustnesscited by 16
  3. 2021k-Mixup Regularization for Deep Learning via Optimal Transportcited by 14
  4. 2023Differentially Private Sharpness-Aware Trainingcited by 14
  5. 2024AttackBench: Evaluating Gradient-based Attacks for Adversarial Examplescited by 14
  6. 2021Adversarial Visual Robustness by Causal Interventioncited by 13
  7. 2021Multi-Expert Adversarial Attack Detection in Person Re-identification Using Context Inconsistencycited by 12
  8. 2024Scaling Laws for Task-Optimized Models of the Primate Visual Ventral Streamcited by 12
  9. 2021Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attackscited by 11
  10. 2023Projected Randomized Smoothing for Certified Adversarial Robustnesscited by 11
  11. 2023Improving Adversarial Transferability via Model Alignmentcited by 10
  12. 2024Game-Theoretic Defenses for Robust Conformal Prediction Against Adversarial Attacks in Medical Imagingcited by 10
  13. 2021Benchmarking the Accuracy and Robustness of Feedback Alignment Algorithmscited by 9
  14. 2023Exploring the Effect of Multi-step Ascent in Sharpness-Aware Minimizationcited by 9
  15. 2023DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detectioncited by 9
  16. 2021Well-classified Examples are Underestimated in Classification with Deep Neural Networkscited by 8
  17. 2022Understanding the effect of sparsity on neural networks robustnesscited by 8
  18. 2022Adversarial Purification with the Manifold Hypothesiscited by 8
  19. 2024Eguard: Defending LLM Embeddings Against Inversion Attacks via Text Mutual Information Optimizationcited by 8
  20. 2023Adversarially Robust Spiking Neural Networks Through Conversioncited by 7
  21. 2024LoRID: Low-Rank Iterative Diffusion for Adversarial Purificationcited by 7
  22. 2022FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systemscited by 6
  23. 2023New Adversarial Image Detection Based on Sentiment Analysiscited by 6
  24. 2023Causality-Aided Trade-off Analysis for Machine Learning Fairnesscited by 6
  25. 2023Frequency Domain Adversarial Training for Robust Volumetric Medical Segmentationcited by 6
  26. 2023Assessing Robustness via Score-Based Adversarial Image Generationcited by 6
  27. 2023Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairlycited by 6
  28. 2021All You Need is RAW: Defending Against Adversarial Attacks with Camera Image Pipelinescited by 5
  29. 2022Exact Spectral Norm Regularization for Neural Networkscited by 5
  30. 2022Fairness Increases Adversarial Vulnerabilitycited by 5
  31. 2023Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothingcited by 5
  32. 2023Your Attack Is Too DUMB: Formalizing Attacker Scenarios for Adversarial Transferabilitycited by 5
  33. 2024AnyPattern: Towards In-context Image Copy Detectioncited by 5
  34. 2024Improving the Transferability of Adversarial Examples by Feature Augmentationcited by 5
  35. 2025NatADiff: Adversarial Boundary Guidance for Natural Adversarial Diffusioncited by 5
  36. 2022Exploring Adversarial Attacks and Defenses in Vision Transformers trained with DINOcited by 4
  37. 2022Understanding Adversarial Robustness of Vision Transformers via Cauchy Problemcited by 4
  38. 2023GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Taskscited by 4

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