mlsanityData to 7 Oct 2026

Adversarial Examples that Fool Detectors

arXiv 1712.02494 ↗First posted 7 Dec 2017cs.CV cs.AI cs.GRcited by 39

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Abstract (arXiv)

An adversarial example is an example that has been adjusted to produce a wrong label when presented to a system at test time. To date, adversarial example constructions have been demonstrated for classifiers, but not for detectors. If adversarial examples that could fool a detector exist, they could be used to (for example) maliciously create security hazards on roads populated with smart vehicles. In this paper, we demonstrate a construction that successfully fools two standard detectors, Faster RCNN and YOLO. The existence of such examples is surprising, as attacking a classifier is very different from attacking a detector, and that the structure of detectors - which must search for their own bounding box, and which cannot estimate that box very accurately - makes it quite likely that adversarial patterns are strongly disrupted. We show that our construction produces adversarial examples that generalize well across sequences digitally, even though large perturbations are needed. We also show that our construction yields physical objects that are adversarial.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Dec 2017.

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Citing papers per year: 2021: 11; 2022: 9; 2023: 7; 2024: 8; 2025: 4.

39 papers.

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  1. 2021Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attackscited by 101
  2. 2021FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attackcited by 74
  3. 2021Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Drivingcited by 45
  4. 2021Robust and Accurate Object Detection via Adversarial Learningcited by 42
  5. 2023Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attackcited by 37
  6. 2021Physical Adversarial Attacks on an Aerial Imagery Object Detectorcited by 35
  7. 2021DetectorGuard: Provably Securing Object Detectors against Localized Patch Hiding Attackscited by 28
  8. 2021Evaluating Adversarial Attacks on Driving Safety in Vision-Based Autonomous Vehiclescited by 24
  9. 2022A Survey on Physical Adversarial Attack in Computer Visioncited by 24
  10. 2022ObjectSeeker: Certifiably Robust Object Detection against Patch Hiding Attacks via Patch-agnostic Maskingcited by 22
  11. 2022Adversarial Attack and Defense of YOLO Detectors in Autonomous Driving Scenarioscited by 20
  12. 2021"Adversarial Examples" for Proof-of-Learningcited by 19
27 more
  1. 2023Adversarial Attacks on Traffic Sign Recognition: A Surveycited by 13
  2. 2021Adversarial Feature Augmentation and Normalization for Visual Recognitioncited by 11
  3. 2022Visual Adversarial Attacks and Defenses in the Physical World: A Surveycited by 11
  4. 2022Federated Learning Attacks and Defenses: A Surveycited by 11
  5. 2023Adversarial Examples in the Physical World: A Surveycited by 10
  6. 2024Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspectivecited by 6
  7. 2021Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Maskscited by 5
  8. 2023A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarkingcited by 5
  9. 2023A Review of Adversarial Attacks in Computer Visioncited by 5
  10. 2023Robust Backdoor Attacks on Object Detection in Real Worldcited by 5
  11. 2022General Adversarial Defense Against Black-box Attacks via Pixel Level and Feature Level Distribution Alignmentscited by 4
  12. 2024Deep Learning Model Security: Threats and Defensescited by 4
  13. 2022Sensor Data Validation and Driving Safety in Autonomous Driving Systemscited by 3
  14. 2022Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Surveycited by 3
  15. 2023To Make Yourself Invisible with Adversarial Semantic Contourscited by 3
  16. 2024Fall Leaf Adversarial Attack on Traffic Sign Classificationcited by 3
  17. 2021Tracklet-Switch Adversarial Attack against Pedestrian Multi-Object Tracking Trackerscited by 1
  18. 2024Time Traveling to Defend Against Adversarial Example Attacks in Image Classificationcited by 1
  19. 2024Distortion-Aware Adversarial Attacks on Bounding Boxes of Object Detectorscited by 1
  20. 2025Mitigation of Camouflaged Adversarial Attacks in Autonomous Vehicles--A Case Study Using CARLA Simulatorcited by 1
  21. 2025Rounding-Guided Backdoor Injection in Deep Learning Model Quantizationcited by 1
  22. 2022Object-fabrication Targeted Attack for Object Detectioncited by 0
  23. 2024BankTweak: Adversarial Attack against Multi-Object Trackers by Manipulating Feature Bankscited by 0
  24. 2024Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attackcited by 0
  25. 2024Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard Baselinescited by 0
  26. 2025Adversarial Attack for RGB-Event based Visual Object Trackingcited by 0
  27. 2025Elytra: A Flexible Framework for Securing Large Vision Systemscited by 0