MobilBye: Attacking ADAS with Camera Spoofing
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Abstract (arXiv)
Advanced driver assistance systems (ADASs) were developed to reduce the number of car accidents by issuing driver alert or controlling the vehicle. In this paper, we tested the robustness of Mobileye, a popular external ADAS. We injected spoofed traffic signs into Mobileye to assess the influence of environmental changes (e.g., changes in color, shape, projection speed, diameter and ambient light) on the outcome of an attack. To conduct this experiment in a realistic scenario, we used a drone to carry a portable projector which projected the spoofed traffic sign on a driving car. Our experiments show that it is possible to fool Mobileye so that it interprets the drone carried spoofed traffic sign as a real traffic sign.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 Jun 2019.
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Citing papers per year: 2021: 4; 2022: 2; 2023: 0; 2024: 0; 2025: 1; 2026: 1.
8 papers.
- 2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defensescited by 30
- 2021They See Me Rollin': Inherent Vulnerability of the Rolling Shutter in CMOS Image Sensorscited by 17
- 2026Safety in Embodied AI: A Survey of Risks, Attacks, and Defensescited by 7
- 2021Light Lies: Optical Adversarial Attackcited by 5
- 2022Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Surveycited by 3
- 2022Explaining RADAR features for detecting spoofing attacks in Connected Autonomous Vehiclescited by 2
- 2021DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencodercited by 1
- 2025PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lightingcited by 0
- 2026Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses28 Mar 2026
- 2025PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lighting8 May 2025
- 2022Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey19 Oct 2022
- 2022Explaining RADAR features for detecting spoofing attacks in Connected Autonomous Vehicles1 Mar 2022
- 2021DriveGuard: Robustification of Automated Driving Systems with Deep Spatio-Temporal Convolutional Autoencoder5 Nov 2021
- 2021Light Lies: Optical Adversarial Attack18 Jun 2021
- 2021Deep Learning-Based Autonomous Driving Systems: A Survey of Attacks and Defenses5 Apr 2021
- 2021They See Me Rollin': Inherent Vulnerability of the Rolling Shutter in CMOS Image Sensors25 Jan 2021