Paper Reading AI Learner

Navigating Threats: A Survey of Physical Adversarial Attacks on LiDAR Perception Systems in Autonomous Vehicles

2024-09-30 15:50:36
Amira Guesmi, Muhammad Shafique

Abstract

Autonomous vehicles (AVs) rely heavily on LiDAR (Light Detection and Ranging) systems for accurate perception and navigation, providing high-resolution 3D environmental data that is crucial for object detection and classification. However, LiDAR systems are vulnerable to adversarial attacks, which pose significant challenges to the safety and robustness of AVs. This survey presents a thorough review of the current research landscape on physical adversarial attacks targeting LiDAR-based perception systems, covering both single-modality and multi-modality contexts. We categorize and analyze various attack types, including spoofing and physical adversarial object attacks, detailing their methodologies, impacts, and potential real-world implications. Through detailed case studies and analyses, we identify critical challenges and highlight gaps in existing attacks for LiDAR-based systems. Additionally, we propose future research directions to enhance the security and resilience of these systems, ultimately contributing to the safer deployment of autonomous vehicles.

Abstract (translated)

自动驾驶车辆(AVs)对激光雷达(LDAR)系统依赖性很大,用于准确感知和导航,提供高分辨率的三维环境数据,这对目标检测和分类至关重要。然而,LDAR系统很容易受到对抗性攻击,这给AV的安全和鲁棒性带来了重大挑战。这项调查对针对基于LDAR感知系统的当前研究格局进行了全面的回顾,涵盖了单模态和多模态情境。我们分类并分析了各种攻击类型,包括伪造和物理对抗性对象攻击,详细描述了它们的攻击方法、影响和潜在的现实世界影响。通过详细的案例研究和分析,我们找出了关键挑战,突出了现有攻击中LDAR系统存在的空白。此外,我们提出了未来研究的方向,以增强这些系统的安全性和鲁棒性,最终为自动驾驶车辆的更安全部署做出贡献。

URL

https://arxiv.org/abs/2409.20426

PDF

https://arxiv.org/pdf/2409.20426.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model LLM Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Robot Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Time_Series Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot