Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models

A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs. In this work, we perform the first stud...

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Bibliographic Details
Published in:arXiv.org 2021-09
Main Authors: Park, Won, Liu, Nan, Chen, Qi Alfred, Z Morley Mao
Format: Article
Language:English
Subjects:
Lidar
Object recognition
Robustness
Sensors
Spoofing
Three dimensional models
Two dimensional models
Online Access:Get full text
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Summary:A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs. In this work, we perform the first study to analyze the robustness of a high-performance, open source sensor fusion model architecture towards adversarial attacks and challenge the popular belief that the use of additional sensors automatically mitigate the risk of adversarial attacks. We find that despite the use of a LIDAR sensor, the model is vulnerable to our purposefully crafted image-based adversarial attacks including disappearance, universal patch, and spoofing. After identifying the underlying reason, we explore some potential defenses and provide some recommendations for improved sensor fusion models.
ISSN:2331-8422
DOI:10.48550/arxiv.2109.06363