Optical Lens Attack on Deep Learning Based Monocular Depth Estimation
Journal:
arXiv
Published Date:
Sep 25, 2024
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.
