Data to 14 Oct 2026

Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

arXiv 1805.06605 ↗First posted 17 May 2018cs.CV cs.LG stat.MLcited by 362

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Cited by 35 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks. Defense-GAN is trained to model the distribution of unperturbed images. At inference time, it finds a close output to a given image which does not contain the adversarial changes. This output is then fed to the classifier. Our proposed method can be used with any classification model and does not modify the classifier structure or training procedure. It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples. We empirically show that Defense-GAN is consistently effective against different attack methods and improves on existing defense strategies. Our code has been made publicly available at https://github.com/kabkabm/defensegan

Key facts. Cited by 362 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 May 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 107; 2022: 79; 2023: 63; 2024: 53; 2025: 40; 2026: 20.

362 papers.

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2023Baseline Defenses for Adversarial Attacks Against Aligned Language Modelscited by 651
2022Diffusion Models for Adversarial Purificationcited by 476
2021GAN Inversion: A Surveycited by 367
2021Improving Robustness using Generated Datacited by 234
2021Adversarial purification with Score-based generative modelscited by 134
2021On the Convergence and Robustness of Adversarial Trainingcited by 130
2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
2021Robust Learning Meets Generative Models: Can Proxy Distributions Improve Adversarial Robustness?cited by 113
2022Guided Diffusion Model for Adversarial Purificationcited by 92
2021Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attackscited by 91
2023Robust Classification via a Single Diffusion Modelcited by 79
2023How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defensescited by 66

38 more

2023Robust Evaluation of Diffusion-Based Adversarial Purificationcited by 65
2022Evaluating the Adversarial Robustness of Adaptive Test-time Defensescited by 64
2021Relating Adversarially Robust Generalization to Flat Minimacited by 57
2021Meta Gradient Adversarial Attackcited by 55
2021Ensembling with Deep Generative Viewscited by 53
2021Adversarial Attacks are Reversible with Natural Supervisioncited by 47
2021Online Adversarial Purification based on Self-Supervisioncited by 46
2022A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologiescited by 44
2021Reveal of Vision Transformers Robustness against Adversarial Attackscited by 43
2021The Dimpled Manifold Model of Adversarial Examples in Machine Learningcited by 41
2021Biometrics: Trust, but Verifycited by 39
2021Adversarial Robustness with Semi-Infinite Constrained Learningcited by 39
2022Experimental quantum adversarial learning with programmable superconducting qubitscited by 39
2021Improving the Transferability of Adversarial Examples with Resized-Diverse-Inputs, Diversity-Ensemble and Region Fittingcited by 37
2023DiffAttack: Evasion Attacks Against Diffusion-Based Adversarial Purificationcited by 36
2022Guided Diffusion Model for Adversarial Purification from Random Noisecited by 35
2022DISCO: Adversarial Defense with Local Implicit Functionscited by 34
2022DensePure: Understanding Diffusion Models towards Adversarial Robustnesscited by 33
2025R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuningcited by 33
2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defensescited by 30
2022Towards Efficient Adversarial Training on Vision Transformerscited by 30
2021A Review of Adversarial Attack and Defense for Classification Methodscited by 29
2023Adversarial Illusions in Multi-Modal Embeddingscited by 29
2021AdvRush: Searching for Adversarially Robust Neural Architecturescited by 28
2023Towards quantum enhanced adversarial robustness in machine learningcited by 28
2022Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systemscited by 27
2021Taxonomy of Machine Learning Safety: A Survey and Primercited by 25
2021Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attackscited by 24
2022Threat Model-Agnostic Adversarial Defense using Diffusion Modelscited by 24
2021Towards Robust Neural Networks via Close-loop Controlcited by 23
2022Towards Understanding and Mitigating Audio Adversarial Examples for Speaker Recognitioncited by 23
2022The Enemy of My Enemy is My Friend: Exploring Inverse Adversaries for Improving Adversarial Trainingcited by 23
2023Defending against Adversarial Audio via Diffusion Modelcited by 23
2025CLIP is Strong Enough to Fight Back: Test-time Counterattacks towards Zero-shot Adversarial Robustness of CLIPcited by 23
2022On the Limitations of Stochastic Pre-processing Defensescited by 21
2021Fighting Gradients with Gradients: Dynamic Defenses against Adversarial Attackscited by 20
2021Robustness and Generalization via Generative Adversarial Trainingcited by 20
2022Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarioscited by 20

50 most cited of 362 All 362 (JSON)