Securing Contrastive mmWave-based Human Activity Recognition against Adversarial Label Flipping
Primary Investigator:
Tao Li
Amit Singha, Ziqian Bi, Tao Li, Yimin Chen, Yanchao Zhang
Abstract
Wireless Human Activity Recognition (HAR), leveraging their non-
intrusive nature, has the potential to revolutionize various sectors,
including healthcare, virtual reality, and surveillance. The advent of
millimeter wave (mmWave) technology has significantly enhanced
the capabilities of wireless HAR systems. This paper presents the
first systematic study on the vulnerabilities of mmWave-based HAR
to label flipping poisoning attacks in the context of supervised
contrastive learning. We identify three label poisoning attacks on
the contrastive mmWave-based HAR and propose corresponding
countermeasures. The efficacy of the attacks and also our coun-
termeasures are experimentally validated on a prototype system.
The attacks and countermeasures can be easily extended to other
wireless HAR systems, thereby promoting security considerations
in system design and deployment.