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arXiv Machine Learning By Xiaoxuan Huang, Jinlong Xu, YiZhe Wang, Meng Zhang, Xian Li, Yuying Bian

FreqSpaNet: Frequency and Spatial Learning of SFPF for Physical Layer Hardware Integrity Detection

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The paper introduces FreqSpaNet, a neural network designed to detect hardware integrity violations in wireless devices by learning spatio-frequency polarization fingerprints (SFPFs). It employs a dual-branch architecture: a frequency branch that captures local variations across neighboring frequencies and a geometry-aware spatial branch that models directional relationships using angular information. The two learned representations are adaptively fused, and complementary pretraining is used to preserve distinct characteristics while capturing shared information, achieving a mean AUROC of 96.31%—9.05 points above the baseline—across seven hardware replacement scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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