D-SLAMSpoof：一种基于动态点云注入的环境无关激光雷达欺骗攻击
D-SLAMSpoof: An Environment-Agnostic LiDAR Spoofing Attack using Dynamic Point Cloud Injection
AI总结：
D-SLAMSpoof是一种通过动态点云注入实现的环境无关激光雷达欺骗攻击，利用扫描匹配原理提升攻击成功率，并提出ISD-SLAM作为仅依赖惯性信号的防御方法，有效缓解位置漂移问题。
摘要：
本文介绍了一种名为动态SLAMSpoof（D-SLAMSpoof）的新攻击方法，该方法能够破坏即使在特征丰富的环境中也能破坏激光雷达SLAM。该攻击利用激光雷达欺骗技术，通过外部激光干扰向激光雷达扫描中注入虚假测量值。通过设计空间注入形状和由扫描匹配原理引导的时间协调动态注入模式，D-SLAMSpoof在现实世界中特征丰富的环境中，如城市和室内空间，显著提高了攻击成功率，而传统激光雷达欺骗方法往往失效。此外，我们提出了一种实用的防御方法ISD-SLAM，该方法仅依赖于自主系统中常见的惯性死 reckoning信号。我们证明ISD-SLAM能够准确检测激光雷达欺骗攻击，包括D-SLAMSpoof，并有效缓解由此导致的位置漂移。我们的发现揭示了基于激光雷达的SLAM固有的漏洞，并介绍了使用仅标准机载传感器的首次实用防御方法，以对抗基于激光雷达的SLAM欺骗，为提高自主系统安全性和可靠性提供了关键见解。
英文摘要：
In this work, we introduce Dynamic SLAMSpoof (D-SLAMSpoof), a novel attack that compromises LiDAR SLAM even in feature-rich environments. The attack leverages LiDAR spoofing, which injects spurious measurements into LiDAR scans through external laser interference. By designing both spatial injection shapes and temporally coordinated dynamic injection patterns guided by scan-matching principles, D-SLAMSpoof significantly improves attack success rates in real-world, feature-rich environments such as urban areas and indoor spaces, where conventional LiDAR spoofing methods often fail. Furthermore, we propose a practical defense method, ISD-SLAM, that relies solely on inertial dead reckoning signals commonly available in autonomous systems. We demonstrate that ISD-SLAM accurately detects LiDAR spoofing attacks, including D-SLAMSpoof, and effectively mitigates the resulting position drift. Our findings expose inherent vulnerabilities in LiDAR-based SLAM and introduce the first practical defense against LiDAR-based SLAM spoofing using only standard onboard sensors, providing critical insights for improving the security and reliability of autonomous systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
