MOBA: A Material-Oriented Backdoor Attack Against LiDAR-Based 3D Object Detection Systems
Saket Sanjeev Chaturvedi, Gaurav Bagwe, Lan Emily Zhang, Pan He, Xiaoyong Yuan
Abstract
LiDAR-based 3D object detection is widely used in safety-critical systems. However, these systems remain vulnerable to backdoor attacks that embed hidden malicious behaviors during training. A key limitation of existing backdoor attacks is their lack of physical realizability, primarily due to the digital-to-physical domain gap. Digital triggers often fail in real-world settings because they overlook material-dependent LiDAR reflection properties. On the other hand, physically constructed triggers are often unoptimized, leading to low effectiveness or easy detectability. This paper introduces Material-Oriented Backdoor Attack (MOBA), a novel framework that bridges the digital–physical gap by explicitly modeling the material properties of real-world triggers. MOBA tackles two key challenges in physical backdoor design: 1) robustness of the trigger material under diverse environmental conditions, 2) alignment between the physical trigger
BibTeX
@inproceedings{aaai2026_mobaamaterialori,
title = {MOBA: A Material-Oriented Backdoor Attack Against LiDAR-Based 3D Object Detection Systems},
author = {Saket Sanjeev Chaturvedi and Gaurav Bagwe and Lan Emily Zhang and Pan He and Xiaoyong Yuan},
booktitle = {AAAI 2026},
year = {2026}
}