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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2507.20657 |
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| _version_ | 1866918105578471424 |
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| author | Loupa, Margarita Argyriou, Antonios Liu, Yanwei |
| author_facet | Loupa, Margarita Argyriou, Antonios Liu, Yanwei |
| contents | 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%. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_20657 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals Loupa, Margarita Argyriou, Antonios Liu, Yanwei Signal Processing 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%. |
| title | The micro-Doppler Attack Against AI-based Human Activity Classification from Wireless Signals |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2507.20657 |