mlsanityData to 14 Oct 2026

FIGhost: Fluorescent Ink-based Stealthy and Flexible Backdoor Attacks on Physical Traffic Sign Recognition

arXiv 2505.12045 ↗First posted 17 May 2025cs.CVcited by 1

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Abstract (arXiv)

Traffic sign recognition (TSR) systems are crucial for autonomous driving but are vulnerable to backdoor attacks. Existing physical backdoor attacks either lack stealth, provide inflexible attack control, or ignore emerging Vision-Large-Language-Models (VLMs). In this paper, we introduce FIGhost, the first physical-world backdoor attack leveraging fluorescent ink as triggers. Fluorescent triggers are invisible under normal conditions and activated stealthily by ultraviolet light, providing superior stealthiness, flexibility, and untraceability. Inspired by real-world graffiti, we derive realistic trigger shapes and enhance their robustness via an interpolation-based fluorescence simulation algorithm. Furthermore, we develop an automated backdoor sample generation method to support three attack objectives. Extensive evaluations in the physical world demonstrate FIGhost's effectiveness against state-of-the-art detectors and VLMs, maintaining robustness under environmental variations and effectively evading existing defenses.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 32 of its 50 references that are in the map. First posted on arXiv 17 May 2025.

Builds on

32 of 50 references.

References · 32

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  2. 2023Visual Instruction Tuningcited by 7,869
  3. 2017Mask R-CNNcited by 7,277
  4. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  5. 2023Improved Baselines with Visual Instruction Tuningcited by 4,418
  6. 2015SSD: Single Shot MultiBox Detectorcited by 3,130
  7. 2015Fast R-CNNcited by 2,933
  8. 2024Qwen2 Technical Reportcited by 2,120
  9. 2017Cascade R-CNN: Delving into High Quality Object Detectioncited by 1,276
  10. 2022YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorscited by 982
  11. 2023VILA: On Pre-training for Visual Language Modelscited by 692
  12. 2023DriveLM: Driving with Graph Visual Question Answeringcited by 536
20 more
  1. 2024YOLOv10: Real-Time End-to-End Object Detectioncited by 515
  2. 2024DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Modelscited by 490
  3. 2019STRIP: A Defence Against Trojan Attacks on Deep Neural Networkscited by 459
  4. 2019Distance-IoU Loss: Faster and Better Learning for Bounding Box Regressioncited by 459
  5. 2024YOLOv11: An Overview of the Key Architectural Enhancementscited by 425
  6. 2019Hidden Trigger Backdoor Attackscited by 371
  7. 2020Invisible Backdoor Attack with Sample-Specific Triggerscited by 353
  8. 2020Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networkscited by 349
  9. 2020Backdoor Attacks Against Deep Learning Systems in the Physical Worldcited by 134
  10. 2021Backdoor Attack in the Physical Worldcited by 84
  11. 2023VIGC: Visual Instruction Generation and Correctioncited by 80
  12. 2023On the Road with GPT-4V(ision): Early Explorations of Visual-Language Model on Autonomous Drivingcited by 80
  13. 2024Video Interpolation with Diffusion Modelscited by 68
  14. 2024Physical Backdoor Attack can Jeopardize Driving with Vision-Large-Language Modelscited by 33
  15. 2022Physical Backdoor Attacks to Lane Detection Systems in Autonomous Drivingcited by 31
  16. 2024The AI Community Building the Future? A Quantitative Analysis of Development Activity on Hugging Face Hubcited by 18
  17. 2024Towards Transferable Attacks Against Vision-LLMs in Autonomous Driving with Typographycited by 18
  18. 2024Physical Backdoor: Towards Temperature-based Backdoor Attacks in the Physical Worldcited by 6
  19. 2024On the Credibility of Backdoor Attacks Against Object Detectors in the Physical Worldcited by 5
  20. 2024LaserGuider: A Laser Based Physical Backdoor Attack against Deep Neural Networkscited by 1

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Citing papers per year: 2026: 1.

1 paper.

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  1. 2025T2I-Based Physical-World Appearance Attack against Traffic Sign Recognition Systems in Autonomous Driving
  2. 2024Detector Collapse: Physical-World Backdooring Object Detection to Catastrophic Overload or Blindness in Autonomous Driving
  3. 2026TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models
  4. 2026DETOUR: A Practical Backdoor Attack against Object Detection ↗
  5. 2025Towards Stealthy and Effective Backdoor Attacks on Lane Detection: A Naturalistic Data Poisoning Approach
  6. 2025CIS-BA: Continuous Interaction Space Based Backdoor Attack for Object Detection in the Real-World
  7. 2025Mitigation of Camouflaged Adversarial Attacks in Autonomous Vehicles--A Case Study Using CARLA Simulator
  8. 2023SoK: A Systematic Evaluation of Backdoor Trigger Characteristics in Image Classification