Adversarial Objects Against LiDAR-Based Autonomous Driving Systems

2019/07/11 by Yulong Cao, Chaowei Xiao, Cao, Yulong +11 · 2 voices · 18 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Adversarial Robustness in Machine Learning #Bacillus and Francisella bacterial research #Computer Vision and Pattern Recognition (cs.CV) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Forensic and Genetic Research #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.CR #cs.CV #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1907.05418

arxiv created 2019/07/11 · openalex publication_date 2019/07/11 · arxiv published 2019/07/11 · arxiv updated 2019/07/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Deep neural networks (DNNs) are found to be vulnerable against adversarial examples, which are carefully crafted inputs with a small magnitude of perturbation aiming to induce arbitrarily incorrect predictions. Recent studies show that adversarial examples can pose a threat to real-world security-critical applications: a "physical adversarial Stop Sign" can be synthesized such that the autonomous driving cars will misrecognize it as others (e.g., a speed limit sign). However, these image-space adversarial examples cannot easily alter 3D scans of widely equipped LiDAR or radar on autonomous vehicles. In this paper, we reveal the potential vulnerabilities of LiDAR-based autonomous driving detection systems, by proposing an optimization based approach LiDAR-Adv to generate adversarial objects that can evade the LiDAR-based detection system under various conditions. We first show the vulnerabilities using a blackbox evolution-based algorithm, and then explore how much a strong adversary can do, using our gradient-based approach LiDAR-Adv. We test the generated adversarial objects on the Baidu Apollo autonomous driving platform and show that such physical systems are indeed vulnerable to the proposed attacks. We also 3D-print our adversarial objects and perform physical experiments to illustrate that such vulnerability exists in the real world. Please find more visualizations and results on the anonymous website: https://sites.google.com/view/lidar-adv.

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• Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures
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• LG-GAN: Label Guided Adversarial Network for Flexible Targeted Attack of Point Cloud-based Deep Networks
• IF-Defense: 3D Adversarial Point Cloud Defense via Implicit Function based Restoration
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