A New Adversarial Perspective for LiDAR-based 3D Object Detection

TL;DR

This paper presents a novel adversarial attack framework using simulated environmental interference like water mist and smoke to deceive LiDAR-based 3D object detection systems in autonomous vehicles.

Contribution

It introduces a new dataset and a GAN-based method to generate adversarial point clouds, revealing vulnerabilities in current detection models.

Findings

Adversarial perturbations significantly reduce detection accuracy.

Simulated environmental effects can deceive LiDAR-based detection models.

The proposed method effectively generates realistic adversarial point clouds.

Abstract

Autonomous vehicles (AVs) rely on LiDAR sensors for environmental perception and decision-making in driving scenarios. However, ensuring the safety and reliability of AVs in complex environments remains a pressing challenge. To address this issue, we introduce a real-world dataset (ROLiD) comprising LiDAR-scanned point clouds of two random objects: water mist and smoke. In this paper, we introduce a novel adversarial perspective by proposing an attack framework that utilizes water mist and smoke to simulate environmental interference. Specifically, we propose a point cloud sequence generation method using a motion and content decomposition generative adversarial network named PCS-GAN to simulate the distribution of random objects. Furthermore, leveraging the simulated LiDAR scanning characteristics implemented with Range Image, we examine the effects of introducing random object perturbations at various positions on the target vehicle. Extensive experiments demonstrate that adversarial perturbations based on random objects effectively deceive vehicle detection and reduce the recognition rate of 3D object detection models.