Abstract
Monocular Depth Estimation (MDE) is a pivotal component of vision-based Autonomous Driving (AD) systems, enabling vehicles to estimate the depth of surrounding objects using a single camera image. This estimation guides essential driving decisions, such as braking before an obstacle or changing lanes to avoid collisions. In this paper, we explore vulnerabilities of MDE algorithms in AD systems, presenting πΏπππ π΄π‘π‘πππ, a novel physical attack that strategically places optical lenses on the camera of an autonomous vehicle to manipulate the perceived object depths. πΏπππ π΄π‘π‘πππ encompasses two attack formats: concave lens attack and convex lens attack, each utilizing different optical lenses to induce false depth perception. We first develop a mathematical model that outlines the parameters of the attack, followed by simulations and real-world evaluations to assess its efficacy on state-of-the-art MDE models. Additionally, we adopt an attack optimization method to further enhance the attack success rate by optimizing the attack focal length. To better evaluate the implications of πΏπππ π΄π‘π‘πππ on AD, we conduct comprehensive end-to-end system simulations using the CARLA platform. The results reveal that πΏπππ π΄π‘π‘πππ can significantly disrupt the depth estimation processes in AD systems, posing a serious threat to their reliability and safety. Finally, we discuss some potential defense methods to mitigate the effects of the proposed attack.
Recommended Citation
C. Zhou et al., "Optical Lens Attack on Monocular Depth Estimation for Autonomous Driving,", vol. 37, no. 4, article no. 111 Association for Computing Machinery, Aug 2018.
Department(s)
Computer Science
Keywords and Phrases
Autonomous Driving; Camera; Monocular Depth Estimation; Autonomous Vehicle; Optical Lens
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
Β© 2025 The Authors, All rights reserved
Publication Date
August 2018
Comments
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from permissions@acm.org.