mlsanityData to 7 Oct 2026

Adversarial Patch

arXiv 1712.09665 ↗First posted 27 Dec 2017cs.CVcited by 514

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Cited by 25 in Multimodal LLMs / vision-language models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We present a method to create universal, robust, targeted adversarial image patches in the real world. The patches are universal because they can be used to attack any scene, robust because they work under a wide variety of transformations, and targeted because they can cause a classifier to output any target class. These adversarial patches can be printed, added to any scene, photographed, and presented to image classifiers; even when the patches are small, they cause the classifiers to ignore the other items in the scene and report a chosen target class. To reproduce the results from the paper, our code is available at https://github.com/tensorflow/cleverhans/tree/master/examples/adversarial_patch

Key facts. Cited by 514 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Dec 2017.

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Citing papers per year: 2021: 105; 2022: 80; 2023: 125; 2024: 72; 2025: 62; 2026: 70.

514 papers.

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  1. 2024HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusalcited by 1,330
  2. 2021Intriguing Properties of Vision Transformerscited by 446
  3. 2023PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Promptscited by 218
  4. 2023To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Nowcited by 204
  5. 2023Image Hijacks: Adversarial Images can Control Generative Models at Runtimecited by 165
  6. 2023Raising the Cost of Malicious AI-Powered Image Editingcited by 160
  7. 2024Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fastcited by 149
  8. 2021AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehiclescited by 132
  9. 2021Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical Worldcited by 127
  10. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
  11. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  12. 2023AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learningcited by 101
38 more
  1. 2022Adversarial Texture for Fooling Person Detectors in the Physical Worldcited by 96
  2. 2021Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognitioncited by 95
  3. 2021Adversarial Sticker: A Stealthy Attack Method in the Physical Worldcited by 92
  4. 2021Improving Transferability of Adversarial Patches on Face Recognition with Generative Modelscited by 80
  5. 2021Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blinkcited by 75
  6. 2021FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attackcited by 74
  7. 2024Exploring the Adversarial Vulnerabilities of Vision-Language-Action Models in Roboticscited by 68
  8. 2021Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detectioncited by 67
  9. 2022Physical Adversarial Attack meets Computer Vision: A Decade Surveycited by 66
  10. 2023Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllabilitycited by 66
  11. 2021PatchCleanser: Certifiably Robust Defense against Adversarial Patches for Any Image Classifiercited by 63
  12. 2023X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detectioncited by 58
  13. 2022Physical Attack on Monocular Depth Estimation with Optimal Adversarial Patchescited by 57
  14. 2021Countering Malicious DeepFakes: Survey, Battleground, and Horizoncited by 56
  15. 2021Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attackscited by 55
  16. 2022Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?cited by 47
  17. 2023Adversarial attacks and defenses in explainable artificial intelligence: A surveycited by 47
  18. 2021Detecting Adversarial Examples Is (Nearly) As Hard As Classifying Themcited by 46
  19. 2021Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Drivingcited by 45
  20. 2021Certified Patch Robustness via Smoothed Vision Transformerscited by 44
  21. 2021Robust Contrastive Learning Using Negative Samples with Diminished Semanticscited by 43
  22. 2022Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learningcited by 42
  23. 2023RoboBEV: Towards Robust Bird's Eye View Perception under Corruptionscited by 42
  24. 2021Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertaintiescited by 41
  25. 2022Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attackscited by 41
  26. 2023Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modelingcited by 41
  27. 2021Harnessing Perceptual Adversarial Patches for Crowd Countingcited by 39
  28. 2021Are Vision Transformers Robust to Patch Perturbations?cited by 39
  29. 2022How to Robustify Black-Box ML Models? A Zeroth-Order Optimization Perspectivecited by 39
  30. 2023Physically Adversarial Infrared Patches with Learnable Shapes and Locationscited by 38
  31. 2022Visual correspondence-based explanations improve AI robustness and human-AI team accuracycited by 37
  32. 2023Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Drivingcited by 37
  33. 2023Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attackcited by 37
  34. 2021TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systemscited by 36
  35. 2021Physical Adversarial Attacks on an Aerial Imagery Object Detectorcited by 35
  36. 2021Dual-Key Multimodal Backdoors for Visual Question Answeringcited by 35
  37. 2024Test-Time Backdoor Attacks on Multimodal Large Language Modelscited by 35
  38. 2021Towards Practical Deployment-Stage Backdoor Attack on Deep Neural Networkscited by 33

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