On Adversarial Attacks In Acoustic Drone Localization

TL;DR

This paper investigates the vulnerability of acoustic drone localization systems to adversarial attacks and proposes a method to mitigate their impact, addressing a gap in security research for acoustic-based drone navigation.

Contribution

It provides the first comprehensive analysis of PGD adversarial attacks on acoustic drone localization and introduces an algorithm to reduce attack effects.

Findings

Adversarial attacks significantly impair acoustic drone localization accuracy.

The proposed recovery algorithm markedly diminishes attack impact.

The study highlights security vulnerabilities in acoustic drone navigation systems.

Abstract

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.