Discovering New Shadow Patterns for Black-Box Attacks on Lane Detection of Autonomous Vehicles
<|reference_start|>Discovering New Shadow Patterns for Black-Box Attacks on Lane Detection of Autonomous Vehicles: Ensuring autonomous vehicle (AV) security remains a critical concern. An area of paramount importance is the study of physical-world adversarial examples (AEs) aimed at exploiting vulnerabilities in perception systems. However, most of the prevailing research on AEs has neglected considerations of stealthiness and legality, resulting in scenarios where human drivers would promptly intervene or attackers would be swiftly detected and punished. These limitations hinder the applicability of such examples in real-life settings. In this paper, we introduce a novel approach to generate AEs using what we term negative shadows: deceptive patterns of light on the road created by strategically blocking sunlight, which then cast artificial lane-like patterns. These shadows are inconspicuous to a driver while deceiving AV perception systems, particularly those reliant on lane detection algorithms. By prioritizing the stealthy nature of attacks to minimize driver interventions and ensuring their legality from an attacker's standpoint, a more plausible range of scenarios is established. In multiple scenarios, including at low speeds, our method shows a high safety violation rate. Using a 20-meter negative shadow, it can direct a vehicle off-road with a 100% violation rate at speeds over 10 mph. Other attack scenarios, such as causing collisions, can be performed with at least 30 meters of negative shadow, achieving a 60-100% success rate. The attack also maintains an average stealthiness of 83.6% as measured through a human subject experiment, ensuring its efficacy in covert settings.<|reference_end|>
@article{mohajeransari2024discovering,
    title={Discovering New Shadow Patterns for Black-Box Attacks on Lane Detection
  of Autonomous Vehicles},
    author={Pedram MohajerAnsari, Alkim Domeke, Jan de Voor, Arkajyoti Mitra,
  Grace Johnson, Amir Salarpour, Habeeb Olufowobi, Mohammad Hamad, and Mert D.
  Pes'e},
    journal={arXiv preprint arXiv:2409.18248},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.18248},
    primaryClass={cs.CR}
}