Physical Backdoor Attacks against mmWave-based Human Activity Recognition

Ziqian Bi, Amit Singha, Hongfei Xue, Tao Li, Yimin Chen, Yanchao Zhang

Abstract: Human Activity Recognition (HAR) using wireless signals like mmWave technology has promising applications in numerous scenarios, including monitoring and surveillance, healthcare, and smart home. Wireless HAR is non-intrusive and can operate in situations where traditional sensors or cameras may fail. However, these systems also introduce new attack surfaces alongside their benefits. Existing security research on wireless HAR primarily focuses on the vulnerabilities of the AI models used by these systems, without addressing the challenges of physically implementing these attacks in real-world scenarios. In this paper, we present the first physical backdoor attack for mmWave-based HAR systems, manipulating physical signals to deceive the systems into producing targeted outputs. Utilizing passive metal reflectors and optimized attacking strategies, our attack is efficient, stealthy, and easy to implement. Tailored experiments on a mmWave HAR prototype demonstrate the high effectiveness of the proposed attack.

External IDs:dblp:conf/icdcs/BiSX00Z25