On Adversarial Attacks in Acoustic Localization

Abstract

Multi-rotor aerial vehicles (drones) are increasingly deployed across diverse domains, where accurate navigation is critical. The limitations of vision-based methods under poor lighting and occlusions have driven growing interest in acoustic sensing as an alternative. However, the security of acoustic-based localization has not been examined. Adversarial attacks pose a serious threat, potentially leading to mission-critical failures and safety risks. While prior research has explored adversarial attacks on vision-based systems, no work has addressed the acoustic setting. In this paper, we present the first comprehensive study of adversarial robustness in acoustic drone localization. We formulate white-box projected gradient descent (PGD) attacks from an external sound source and show their significant impact on localization accuracy. Furthermore, we propose a novel defense algorithm based on rotor phase modulation, capable of effectively recovering clean signals and mitigating adversarial degradation. Our results highlight both the vulnerability of acoustic localization and the potential for robust defense strategies.