Universal targeted attacks against mmWave-based human activity recognition system

Abstract

Millimeter wave (mmWave)-based human activity recognition (HAR) systems have emerged in recent years due to their better privacy preservation and higher-resolution sensing. However, these systems are vulnerable to adversarial attacks. In this work, we propose a universal targeted attack method for mmWave-based HAR system. In particular, a universal perturbation is generated in advance which can be added to new-coming mmWave data to deceive the HAR system, causing it to output our desired label. We validate our proposed attack using a public mmWave dataset. We demonstrate the effectiveness of our proposed universal attack with a high attack success rate of over 95%.