Wi-Spoof: Generating adversarial wireless signals to deceive Wi-Fi sensing systems

Abstract

The rise of Wi-Fi sensing applications leveraging Channel State Information (CSI) from ambient wireless signals has opened up extensive opportunities for human activity and identity recognition. However, this advancement raises serious privacy concerns, as sensitive personal data can be inferred by applying advanced Machine Learning (ML) algorithms to CSI data. In response, researchers have explored adversarial techniques to degrade Wi-Fi sensing accuracy and protect privacy, often by interfering with or corrupting CSI. This paper introduces Wi-Spoof, a novel approach for spoofing CSI to deceive Wi-Fi-based Human Activity Recognition (HAR) systems. Wi-Spoof manipulates Wi-Fi transmission power to inject noise into the CSI and employs a pseudo-Pulse Width Modulation (PWM) scheme to generate controlled, adversarial CSI. Using commercially available hardware, we experimentally demonstrate that Wi-Spoof can achieve targeted misclassification in a state-of-the-art HAR system with a 93% success rate. Our approach is validated on a widely recognised public dataset and further supported by extensive local experiments, underscoring Wi-Spoof’s effectiveness in steering HAR predictions to specified outcomes.