# Revisiting Physical-World Adversarial Attack on Traffic Sign Recognition: A Commercial Systems Perspective

arXiv: https://arxiv.org/abs/2409.09860 · HTML: https://mlsanity.com/paper/2409.09860/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 82 references that are in the map.
- First posted on arXiv 15 Sep 2024.

## Builds on (most cited first, 30 of 37)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Object Detection in 20 Years: A Survey](https://mlsanity.com/paper/1905.05055/) (2019)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://mlsanity.com/paper/2106.00666/) (2021)
- [Physical Adversarial Examples for Object Detectors](https://mlsanity.com/paper/1807.07769/) (2018)
- [ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector](https://mlsanity.com/paper/1804.05810/) (2018)
- [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://mlsanity.com/paper/1907.06826/) (2019)
- [Benchmarking Detection Transfer Learning with Vision Transformers](https://mlsanity.com/paper/2111.11429/) (2021)
- [Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks](https://mlsanity.com/paper/2106.09249/) (2021)
- [Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon](https://mlsanity.com/paper/2203.03818/) (2022)
- [Online Multi-Object Tracking with Dual Matching Attention Networks](https://mlsanity.com/paper/1902.00749/) (2019)
- [Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink](https://mlsanity.com/paper/2103.06504/) (2021)
- [Interpretable Deep Learning under Fire](https://mlsanity.com/paper/1812.00891/) (2018)
- [Dirty Road Can Attack: Security of Deep Learning based Automated Lane Centering under Physical-World Attack](https://mlsanity.com/paper/2009.06701/) (2020)
- [Black-box Adversarial Attacks on Video Recognition Models](https://mlsanity.com/paper/1904.05181/) (2019)
- [SLAP: Improving Physical Adversarial Examples with Short-Lived Adversarial Perturbations](https://mlsanity.com/paper/2007.04137/) (2020)
- [NO Need to Worry about Adversarial Examples in Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/1707.03501/) (2017)
- [Adversarial Examples that Fool Detectors](https://mlsanity.com/paper/1712.02494/) (2017)
- [Invisible Perturbations: Physical Adversarial Examples Exploiting the Rolling Shutter Effect](https://mlsanity.com/paper/2011.13375/) (2020)
- [Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack](https://mlsanity.com/paper/2308.11894/) (2023)
- [You Can't See Me: Physical Removal Attacks on LiDAR-based Autonomous Vehicles Driving Frameworks](https://mlsanity.com/paper/2210.09482/) (2022)

## Built on by (most cited first, 6 of 6)

- [Safety2Drive: Safety-Critical Scenario Benchmark for the Evaluation of Autonomous Driving](https://mlsanity.com/paper/2505.13872/) (2025)
- [Temporal Misalignment Attacks against Multimodal Perception in Autonomous Driving](https://mlsanity.com/paper/2507.09095/) (2025)
- [Sequence-Preserving Dual-FoV Defense for Traffic Sign and Light Recognition in Autonomous Vehicles](https://mlsanity.com/paper/2510.02642/) (2025)
- [T2I-Based Physical-World Appearance Attack against Traffic Sign Recognition Systems in Autonomous Driving](https://mlsanity.com/paper/2511.12956/) (2025)
- [AdROD: HyperNetwork-based Adversarially Robust Object Detection for Autonomous Driving](https://mlsanity.com/paper/2608.16031/) (2026)
- [Adversarial Calibration Attack on Autonomous Vehicles](https://mlsanity.com/paper/2608.28778/) (2026)

## Abstract (arXiv)

Traffic Sign Recognition (TSR) is crucial for safe and correct driving automation. Recent works revealed a general vulnerability of TSR models to physical-world adversarial attacks, which can be low-cost, highly deployable, and capable of causing severe attack effects such as hiding a critical traffic sign or spoofing a fake one. However, so far existing works generally only considered evaluating the attack effects on academic TSR models, leaving the impacts of such attacks on real-world commercial TSR systems largely unclear. In this paper, we conduct the first large-scale measurement of physical-world adversarial attacks against commercial TSR systems. Our testing results reveal that it is possible for existing attack works from academia to have highly reliable (100%) attack success against certain commercial TSR system functionality, but such attack capabilities are not generalizable, leading to much lower-than-expected attack success rates overall. We find that one potential major factor is a spatial memorization design that commonly exists in today's commercial TSR systems. We design new attack success metrics that can mathematically model the impacts of such design on the TSR system-level attack success, and use them to revisit existing attacks. Through these efforts, we uncover 7 novel observations, some of which directly challenge the observations or claims in prior works due to the introduction of the new metrics.