mlsanityData to 7 Oct 2026

The Translucent Patch: A Physical and Universal Attack on Object Detectors

arXiv 2012.12528 ↗First posted 23 Dec 2020cs.CV cs.CR cs.LGcited by 62

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Abstract (arXiv)

Physical adversarial attacks against object detectors have seen increasing success in recent years. However, these attacks require direct access to the object of interest in order to apply a physical patch. Furthermore, to hide multiple objects, an adversarial patch must be applied to each object. In this paper, we propose a contactless translucent physical patch containing a carefully constructed pattern, which is placed on the camera's lens, to fool state-of-the-art object detectors. The primary goal of our patch is to hide all instances of a selected target class. In addition, the optimization method used to construct the patch aims to ensure that the detection of other (untargeted) classes remains unharmed. Therefore, in our experiments, which are conducted on state-of-the-art object detection models used in autonomous driving, we study the effect of the patch on the detection of both the selected target class and the other classes. We show that our patch was able to prevent the detection of 42.27% of all stop sign instances while maintaining high (nearly 80%) detection of the other classes.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Dec 2020.

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Citing papers per year: 2021: 10; 2022: 11; 2023: 19; 2024: 10; 2025: 7; 2026: 5.

62 papers.

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  1. 2021Advances in adversarial attacks and defenses in computer vision: A surveycited by 127
  2. 2021Adversarial Sticker: A Stealthy Attack Method in the Physical Worldcited by 92
  3. 2022Physical Adversarial Attack meets Computer Vision: A Decade Surveycited by 66
  4. 2023Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Drivingcited by 37
  5. 2023Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attackcited by 37
  6. 2024Effectiveness Assessment of Recent Large Vision-Language Modelscited by 29
  7. 2022Phantom Sponges: Exploiting Non-Maximum Suppression to Attack Deep Object Detectorscited by 28
  8. 2022HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Designcited by 27
  9. 2021Robust Physical-World Attacks on Face Recognitioncited by 26
  10. 2022A Survey on Physical Adversarial Attack in Computer Visioncited by 24
  11. 2022SoK: On the Semantic AI Security in Autonomous Drivingcited by 22
  12. 2022Benchmarking Adversarial Patch Against Aerial Detectioncited by 21
38 more
  1. 2023Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlookcited by 20
  2. 2021Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Modelcited by 19
  3. 2023Query-Efficient Decision-based Black-Box Patch Attackcited by 19
  4. 2021Stereoscopic Universal Perturbations across Different Architectures and Datasetscited by 17
  5. 2022Adversarial Patch Attacks and Defences in Vision-Based Tasks: A Surveycited by 17
  6. 2021SoK: Anti-Facial Recognition Technologycited by 16
  7. 2021LTD: Low Temperature Distillation for Gradient Masking-free Adversarial Trainingcited by 13
  8. 2023Adversarial Attacks on Traffic Sign Recognition: A Surveycited by 13
  9. 2021Adversarial Token Attacks on Vision Transformerscited by 11
  10. 2022Visual Adversarial Attacks and Defenses in the Physical World: A Surveycited by 11
  11. 2023Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical Worldcited by 11
  12. 2024Environmental Matching Attack Against Unmanned Aerial Vehicles Object Detectioncited by 11
  13. 2023Adversarial Examples in the Physical World: A Surveycited by 10
  14. 2023Adversarial Infrared Curves: An Attack on Infrared Pedestrian Detectors in the Physical Worldcited by 10
  15. 2022Controllable Evaluation and Generation of Physical Adversarial Patch on Face Recognitioncited by 8
  16. 2023State-of-the-art optical-based physical adversarial attacks for deep learning computer vision systemscited by 8
  17. 2021IPatch: A Remote Adversarial Patchcited by 7
  18. 2021Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?cited by 7
  19. 2023Adversarial Attack with Raindropscited by 7
  20. 2022Attacking Object Detector Using A Universal Targeted Label-Switch Patchcited by 6
  21. 2023Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systemscited by 6
  22. 2023A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarkingcited by 5
  23. 2024I Don't Know You, But I Can Catch You: Real-Time Defense against Diverse Adversarial Patches for Object Detectorscited by 5
  24. 2022The Security of Deep Learning Defences for Medical Imagingcited by 4
  25. 2023Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical worldcited by 4
  26. 2025ETA: Energy-based Test-time Adaptation for Depth Completioncited by 4
  27. 2024Out-of-Bounding-Box Triggers: A Stealthy Approach to Cheat Object Detectorscited by 3
  28. 2024Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera Systemscited by 3
  29. 2023Why Don't You Clean Your Glasses? Perception Attacks with Dynamic Optical Perturbationscited by 2
  30. 2023QuantAttack: Exploiting Dynamic Quantization to Attack Vision Transformerscited by 2
  31. 2024DeSparsify: Adversarial Attack Against Token Sparsification Mechanisms in Vision Transformerscited by 2
  32. 2024The Fluorescent Veil: A Stealthy and Effective Physical Adversarial Patch Against Traffic Sign Recognitioncited by 2
  33. 2023X-Detect: Explainable Adversarial Patch Detection for Object Detectors in Retailcited by 1
  34. 2023Generating Visually Realistic Adversarial Patchcited by 1
  35. 2025A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challengescited by 1
  36. 2025Adversarial Examples in Environment Perception for Automated Driving (Review)cited by 1
  37. 2025IAP: Invisible Adversarial Patch Attack through Perceptibility-Aware Localization and Perturbation Optimizationcited by 1
  38. 2023Imperceptible CMOS camera dazzle for adversarial attacks on deep neural networkscited by 0

50 most cited of 62 All 62 (JSON)