A subset of Human Activity Classification (HAC) systems are based on AI algorithms that use passively collected wireless signals. This paper presents the micro-Doppler attack targeting HAC from wireless orthogonal frequency division multiplexing (OFDM) signals. The attack is executed by inserting artificial variations in a transmitted OFDM waveform to alter its micro-Doppler signature when it reflects off a human target. We investigate two variants of our scheme that manipulate the waveform at different time scales resulting in altered receiver spectrograms. HAC accuracy with a deep convolutional neural network (CNN) can be reduced to less than 10%.
@article{arxiv.2507.20657,
title = {The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals},
author = {Margarita Loupa and Antonios Argyriou and Yanwei Liu},
journal= {arXiv preprint arXiv:2507.20657},
year = {2025}
}