研究声学无人机定位的对抗攻击，提出恢复算法降低攻击影响。
On Adversarial Attacks In Acoustic Drone Localization
- 用PGD攻击声学定位系统，分析其脆弱性
- 提出扰动恢复算法，显著降低攻击效果
- 为非视觉导航安全提供新思路，适合安全研究者
多旋翼自主飞行器（MAVs，即“无人机”）近年来因在农业、商业配送、搜救等领域的广泛应用而备受关注。视觉导航受光照和遮挡影响较大，促使研究转向声学感知等其他模态。在非受控环境中大规模使用无人机时，其导航系统可能面临对抗攻击威胁，导致任务失败、安全漏洞甚至危及操作员与旁观者。尽管声学无人机定位已有进展，但现有对抗攻击研究仅聚焦于视觉系统。本文首次系统分析了PGD对抗攻击对声学无人机定位的影响，并提出一种对抗扰动恢复算法，可显著减弱攻击影响。
原文摘要 · Abstract (English)
Multi-rotor aerial autonomous vehicles (MAVs, more widely known as "drones") have been generating increased interest in recent years due to their growing applicability in a vast and diverse range of fields (e.g., agriculture, commercial delivery, search and rescue). The sensitivity of visual-based methods to lighting conditions and occlusions had prompted growing study of navigation reliant on other modalities, such as acoustic sensing. A major concern in using drones in scale for tasks in non-controlled environments is the potential threat of adversarial attacks over their navigational systems, exposing users to mission-critical failures, security breaches, and compromised safety outcomes that can endanger operators and bystanders. While previous work shows impressive progress in acoustic-based drone localization, prior research in adversarial attacks over drone navigation only addresses visual sensing-based systems. In this work, we aim to compensate for this gap by supplying a comprehensive analysis of the effect of PGD adversarial attacks over acoustic drone localization. We furthermore develop an algorithm for adversarial perturbation recovery, capable of markedly diminishing the affect of such attacks in our setting.
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