mlsanityData to 7 Oct 2026

Adversarial Attacks on Traffic Sign Recognition: A Survey

arXiv 2307.08278 ↗First posted 17 Jul 2023cs.CV cs.CR cs.LGcited by 13

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Abstract (arXiv)

Traffic sign recognition is an essential component of perception in autonomous vehicles, which is currently performed almost exclusively with deep neural networks (DNNs). However, DNNs are known to be vulnerable to adversarial attacks. Several previous works have demonstrated the feasibility of adversarial attacks on traffic sign recognition models. Traffic signs are particularly promising for adversarial attack research due to the ease of performing real-world attacks using printed signs or stickers. In this work, we survey existing works performing either digital or real-world attacks on traffic sign detection and classification models. We provide an overview of the latest advancements and highlight the existing research areas that require further investigation.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). Builds on 36 of its 67 references that are in the map. First posted on arXiv 17 Jul 2023.

Builds on

36 of 67 references.

References · 36

  1. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,617
  2. 2014Explaining and Harnessing Adversarial Examplescited by 7,287
  3. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,326
  4. 2013Intriguing properties of neural networkscited by 5,114
  5. 2016Towards Evaluating the Robustness of Neural Networkscited by 2,976
  6. 2018YOLOv3: An Incremental Improvementcited by 2,652
  7. 2016YOLO9000: Better, Faster, Strongercited by 1,429
  8. 2016Practical Black-Box Attacks against Machine Learningcited by 893
  9. 2015The Limitations of Deep Learning in Adversarial Settingscited by 784
  10. 2016Universal adversarial perturbationscited by 774
  11. 2019Square Attack: a query-efficient black-box adversarial attack via random searchcited by 682
  12. 2015Distillation as a Defense to Adversarial Perturbations against Deep Neural Networkscited by 675
24 more
  1. 2017Adversarial Patchcited by 514
  2. 2019Simple Black-box Adversarial Attackscited by 249
  3. 2018Physical Adversarial Examples for Object Detectorscited by 163
  4. 2018ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detectorcited by 159
  5. 2017DSSD : Deconvolutional Single Shot Detectorcited by 130
  6. 2018LaVAN: Localized and Visible Adversarial Noisecited by 112
  7. 2022Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenoncited by 101
  8. 2021Adversarial Sticker: A Stealthy Attack Method in the Physical Worldcited by 92
  9. 2020The Translucent Patch: A Physical and Universal Attack on Object Detectorscited by 62
  10. 2021Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attackscited by 55
  11. 2018DARTS: Deceiving Autonomous Cars with Toxic Signscited by 53
  12. 2018Provable Robustness of ReLU networks via Maximization of Linear Regionscited by 51
  13. 2017Adversarial Examples that Fool Detectorscited by 39
  14. 2019Fooling a Real Car with Adversarial Traffic Signscited by 29
  15. 2022Fooling the Eyes of Autonomous Vehicles: Robust Physical Adversarial Examples Against Traffic Sign Recognition Systemscited by 27
  16. 2018Rogue Signs: Deceiving Traffic Sign Recognition with Malicious Ads and Logoscited by 14
  17. 2022Feasibility of Inconspicuous GAN-generated Adversarial Patches against Object Detectioncited by 13
  18. 2022GLARE: A Dataset for Traffic Sign Detection in Sun Glarecited by 8
  19. 2017Standard detectors aren't (currently) fooled by physical adversarial stop signscited by 7
  20. 2020Targeted Physical-World Attention Attack on Deep Learning Models in Road Sign Recognitioncited by 7
  21. 2023Adversarial Attack with Raindropscited by 7
  22. 2022Suppress with a Patch: Revisiting Universal Adversarial Patch Attacks against Object Detectioncited by 6
  23. 2023Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Studycited by 5
  24. 2017Building Robust Deep Neural Networks for Road Sign Detectioncited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 5; 2026: 3.

13 papers.

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  1. 2024A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offscited by 5
  2. 2024Fall Leaf Adversarial Attack on Traffic Sign Classificationcited by 3
  3. 2025Runtime Safety Monitoring of Deep Neural Networks for Perception: A Surveycited by 2
  4. 2024Time Traveling to Defend Against Adversarial Example Attacks in Image Classificationcited by 1
  5. 2024MAGIC: Mastering Physical Adversarial Generation in Context through Collaborative LLM Agentscited by 1
  6. 2025Mitigation of Camouflaged Adversarial Attacks in Autonomous Vehicles--A Case Study Using CARLA Simulatorcited by 1
  7. 2025Adversarial Universal Stickers: Universal Perturbation Attacks on Traffic Sign using Stickerscited by 1
  8. 2026On the Assessment of Sensitivity of Autonomous Vehicle Perceptioncited by 1
  9. 2024Evaluating Adversarial Attacks on Traffic Sign Classifiers beyond Standard Baselinescited by 0
  10. 2025Elytra: A Flexible Framework for Securing Large Vision Systemscited by 0
  11. 2025TrajSyn: Privacy-Preserving Dataset Distillation from Federated Model Trajectories for Server-Side Adversarial Trainingcited by 0
  12. 2026Robust Vision Systems for Connected and Autonomous Vehicles: Security Challenges and Attack Vectorscited by 0
1 more
  1. 2026A combination of noise and bilateral filters achieve supralinear and scalable adversarial robustness in CNNscited by 0