{"ID":2889556,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.20657","arxiv_id":"2507.20657","title":"The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals","abstract":"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%.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2507.20657","url_pdf":"https://arxiv.org/pdf/2507.20657v1","authors":"[\"Margarita Loupa\",\"Antonios Argyriou\",\"Yanwei Liu\"]","published":"2025-07-28T09:23:18Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[\"Convolutional Neural Network\"]","has_code":false}