Optical Lens Attack on Deep Learning Based Monocular Depth Estimation

• URL: http://arxiv.org/abs/2409.17376v1
• Date: Wed, 25 Sep 2024 21:44:14 GMT
• Title: Optical Lens Attack on Deep Learning Based Monocular Depth Estimation
• Authors: Ce Zhou, Qiben Yan, Daniel Kent, Guangjing Wang, Ziqi Zhang, Hayder Radha,
• Abstract summary: We introduce LensAttack, a physical attack that involves strategically placing optical lenses on the camera of an autonomous vehicle. We simulate the attack and evaluate its real-world performance in driving scenarios to demonstrate its effect on state-of-the-art MDE models.
• Score: 12.91903197852097
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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