mlsanityData to 7 Oct 2026

Generating Adversarial Examples with Adversarial Networks

arXiv 1801.02610 ↗First posted 8 Jan 2018cs.CR cs.CV stat.MLcited by 256

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Abstract (arXiv)

Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high perceptual quality and more efficiently requires more research efforts. In this paper, we propose AdvGAN to generate adversarial examples with generative adversarial networks (GANs), which can learn and approximate the distribution of original instances. For AdvGAN, once the generator is trained, it can generate adversarial perturbations efficiently for any instance, so as to potentially accelerate adversarial training as defenses. We apply AdvGAN in both semi-whitebox and black-box attack settings. In semi-whitebox attacks, there is no need to access the original target model after the generator is trained, in contrast to traditional white-box attacks. In black-box attacks, we dynamically train a distilled model for the black-box model and optimize the generator accordingly. Adversarial examples generated by AdvGAN on different target models have high attack success rate under state-of-the-art defenses compared to other attacks. Our attack has placed the first with 92.76% accuracy on a public MNIST black-box attack challenge.

Key facts. Cited by 256 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Jan 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 60; 2022: 53; 2023: 60; 2024: 42; 2025: 26; 2026: 15.

256 papers.

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  1. 2021Feature Importance-aware Transferable Adversarial Attackscited by 125
  2. 2023End-to-End Diffusion Latent Optimization Improves Classifier Guidancecited by 107
  3. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  4. 2021Improving Transferability of Adversarial Patches on Face Recognition with Generative Modelscited by 80
  5. 2022Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfercited by 77
  6. 2023Unleashing the potential of prompt engineering for large language modelscited by 72
  7. 2021Intermediate Layer Optimization for Inverse Problems using Deep Generative Modelscited by 70
  8. 2022Physical Adversarial Attack meets Computer Vision: A Decade Surveycited by 66
  9. 2022Evaluating the Susceptibility of Pre-Trained Language Models via Handcrafted Adversarial Examplescited by 65
  10. 2023How Deep Learning Sees the World: A Survey on Adversarial Attacks & Defensescited by 65
  11. 2022Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patchescited by 57
  12. 2021Fooling thermal infrared pedestrian detectors in real world using small bulbscited by 51
38 more
  1. 2022AdvDO: Realistic Adversarial Attacks for Trajectory Predictioncited by 48
  2. 2022Fairness-aware Adversarial Perturbation Towards Bias Mitigation for Deployed Deep Modelscited by 47
  3. 2023AdvDiff: Generating Unrestricted Adversarial Examples using Diffusion Modelscited by 46
  4. 2022Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioningcited by 45
  5. 2022A Survey of Robust Adversarial Training in Pattern Recognition: Fundamental, Theory, and Methodologiescited by 44
  6. 20213D-VField: Adversarial Augmentation of Point Clouds for Domain Generalization in 3D Object Detectioncited by 43
  7. 2023A Survey on Transferability of Adversarial Examples across Deep Neural Networkscited by 43
  8. 2022Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real Worldcited by 42
  9. 2021On the Certified Robustness for Ensemble Models and Beyondcited by 40
  10. 2021Adversarial Robustness with Semi-Infinite Constrained Learningcited by 39
  11. 2023Physically Adversarial Infrared Patches with Learnable Shapes and Locationscited by 38
  12. 2021TRS: Transferability Reduced Ensemble via Encouraging Gradient Diversity and Model Smoothnesscited by 37
  13. 2021TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systemscited by 36
  14. 2023Diversifying the High-level Features for better Adversarial Transferabilitycited by 34
  15. 2023DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protectioncited by 34
  16. 2024Revisiting Adversarial Training at Scalecited by 33
  17. 2022LOT: Layer-wise Orthogonal Training on Improving ℓ2 Certified Robustnesscited by 32
  18. 2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defensescited by 29
  19. 2023Generalizable Black-Box Adversarial Attack with Meta Learningcited by 29
  20. 2021Grey-box Adversarial Attack And Defence For Sentiment Classificationcited by 28
  21. 2021Defending Against Image Corruptions Through Adversarial Augmentationscited by 28
  22. 2021AdvRush: Searching for Adversarially Robust Neural Architecturescited by 28
  23. 2021Robust Feature-Level Adversaries are Interpretability Toolscited by 27
  24. 2023Rethinking the Backward Propagation for Adversarial Transferabilitycited by 27
  25. 2023Towards quantum enhanced adversarial robustness in machine learningcited by 26
  26. 2022Restricted Black-box Adversarial Attack Against DeepFake Face Swappingcited by 25
  27. 2021LAFEAT: Piercing Through Adversarial Defenses with Latent Featurescited by 24
  28. 2021Knowledge Enhanced Machine Learning Pipeline against Diverse Adversarial Attackscited by 24
  29. 2022A Survey on Physical Adversarial Attack in Computer Visioncited by 24
  30. 2021Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attackcited by 23
  31. 2023Zero-shot Model Diagnosiscited by 23
  32. 2024Learning to Transform Dynamically for Better Adversarial Transferabilitycited by 23
  33. 2023Enhancing Generalization of Universal Adversarial Perturbation through Gradient Aggregationcited by 22
  34. 2024Watermark-embedded Adversarial Examples for Copyright Protection against Diffusion Modelscited by 22
  35. 2024Adversarial Attacks of Vision Tasks in the Past 10 Years: A Surveycited by 22
  36. 2021Creativity and Machine Learning: A Surveycited by 21
  37. 2022Adversarially-Aware Robust Object Detectorcited by 20
  38. 2023Defending against Adversarial Audio via Diffusion Modelcited by 20

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