# Physical Adversarial Attack on Vehicle Detector in the Carla Simulator

arXiv: https://arxiv.org/abs/2007.16118 · HTML: https://mlsanity.com/paper/2007.16118/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2020.

## Built on by (most cited first, 30 of 38)

- [AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles](https://mlsanity.com/paper/2101.06549/) (2021)
- [FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack](https://mlsanity.com/paper/2109.07193/) (2021)
- [Physical Adversarial Attack meets Computer Vision: A Decade Survey](https://mlsanity.com/paper/2209.15179/) (2022)
- [Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks](https://mlsanity.com/paper/2108.06179/) (2021)
- [DTA: Physical Camouflage Attacks using Differentiable Transformation Network](https://mlsanity.com/paper/2203.09831/) (2022)
- [Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving](https://mlsanity.com/paper/2403.17301/) (2024)
- [ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion](https://mlsanity.com/paper/2308.07009/) (2023)
- [Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses](https://mlsanity.com/paper/2104.01789/) (2021)
- [On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving](https://mlsanity.com/paper/2201.01850/) (2022)
- [A Survey on Physical Adversarial Attack in Computer Vision](https://mlsanity.com/paper/2209.14262/) (2022)
- [Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook](https://mlsanity.com/paper/2308.06173/) (2023)
- [RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation](https://mlsanity.com/paper/2402.15853/) (2024)
- [DeformRS: Certifying Input Deformations with Randomized Smoothing](https://mlsanity.com/paper/2107.00996/) (2021)
- [Object Detectors in the Open Environment: Challenges, Solutions, and Outlook](https://mlsanity.com/paper/2403.16271/) (2024)
- [Visual Adversarial Attacks and Defenses in the Physical World: A Survey](https://mlsanity.com/paper/2211.01671/) (2022)
- [Adv3D: Generating 3D Adversarial Examples for 3D Object Detection in Driving Scenarios with NeRF](https://mlsanity.com/paper/2309.01351/) (2023)
- [Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation](https://mlsanity.com/paper/2311.01446/) (2023)
- [CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models](https://mlsanity.com/paper/2206.04365/) (2022)
- [PlanT 2.0: Exposing Biases and Structural Flaws in Closed-Loop Driving](https://mlsanity.com/paper/2511.07292/) (2025)
- [Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?](https://mlsanity.com/paper/2108.07229/) (2021)
- [Flexible Physical Camouflage Generation Based on a Differential Approach](https://mlsanity.com/paper/2402.13575/) (2024)
- [3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation](https://mlsanity.com/paper/2507.01367/) (2025)
- [Exploring the Physical World Adversarial Robustness of Vehicle Detection](https://mlsanity.com/paper/2308.03476/) (2023)
- [Embodied Laser Attack:Leveraging Scene Priors to Achieve Agent-based Robust Non-contact Attacks](https://mlsanity.com/paper/2312.09554/) (2023)
- [CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors](https://mlsanity.com/paper/2409.17963/) (2024)
- [Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2210.11237/) (2022)
- [Attention-Based Real-Time Defenses for Physical Adversarial Attacks in Vision Applications](https://mlsanity.com/paper/2311.11191/) (2023)
- [3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving](https://mlsanity.com/paper/2507.09993/) (2025)
- [Toward Robust and Accurate Adversarial Camouflage Generation against Vehicle Detectors](https://mlsanity.com/paper/2411.10029/) (2024)
- [A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges](https://mlsanity.com/paper/2503.00384/) (2025)

## Abstract (arXiv)

In this paper, we tackle the issue of physical adversarial examples for object detectors in the wild. Specifically, we proposed to generate adversarial patterns to be applied on vehicle surface so that it's not recognizable by detectors in the photo-realistic Carla simulator. Our approach contains two main techniques, an Enlarge-and-Repeat process and a Discrete Searching method, to craft mosaic-like adversarial vehicle textures without access to neither the model weight of the detector nor a differential rendering procedure. The experimental results demonstrate the effectiveness of our approach in the simulator.