Optical Lens Attack on Deep Learning Based Monocular Depth Estimation
<|reference_start|>Optical Lens Attack on Deep Learning Based Monocular Depth Estimation: Monocular Depth Estimation (MDE) plays a crucial role in vision-based Autonomous Driving (AD) systems. It utilizes a single-camera image to determine the depth of objects, facilitating driving decisions such as braking a few meters in front of a detected obstacle or changing lanes to avoid collision. In this paper, we investigate the security risks associated with monocular vision-based depth estimation algorithms utilized by AD systems. By exploiting the vulnerabilities of MDE and the principles of optical lenses, we introduce LensAttack, a physical attack that involves strategically placing optical lenses on the camera of an autonomous vehicle to manipulate the perceived object depths. LensAttack encompasses two attack formats: concave lens attack and convex lens attack, each utilizing different optical lenses to induce false depth perception. We begin by constructing a mathematical model of our attack, incorporating various attack parameters. Subsequently, we simulate the attack and evaluate its real-world performance in driving scenarios to demonstrate its effect on state-of-the-art MDE models. The results highlight the significant impact of LensAttack on the accuracy of depth estimation in AD systems.<|reference_end|>
@article{zhou2024optical,
    title={Optical Lens Attack on Deep Learning Based Monocular Depth Estimation},
    author={Ce Zhou (1), Qiben Yan (1), Daniel Kent (1), Guangjing Wang (1), Ziqi
  Zhang (2), Hayder Radha (1) ((1) Michigan State University, (2) Peking
  University)},
    journal={arXiv preprint arXiv:2409.17376},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17376},
    primaryClass={cs.CR cs.CV}
}