CrossRF: A Domain-Invariant Deep Learning Approach for RF Fingerprinting

• URL: http://arxiv.org/abs/2505.18200v1
• Date: Wed, 21 May 2025 12:20:10 GMT
• Title: CrossRF: A Domain-Invariant Deep Learning Approach for RF Fingerprinting
• Authors: Fahrettin Emin Tiras, Hayriye Serra Altinoluk,
• Abstract summary: CrossRF is a domain-invariant deep learning approach that addresses the problem of cross-channel RF fingerprinting.<n>We validate our approach using the UAVSig dataset, comprising real-world over-the-air RF signals from identical drone models.<n>Experiments show CrossRF's efficiency, achieving up to 99.03% accuracy when adapting from Channel 3 to Channel 4, compared to only 26.39% using conventional methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Radio Frequency (RF) fingerprinting offers a promising approach for drone identification and security, although it suffers from significant performance degradation when operating on different transmission channels. This paper presents CrossRF, a domain-invariant deep learning approach that addresses the problem of cross-channel RF fingerprinting for Unmanned Aerial Vehicle (UAV) identification. Our approach aims to minimize the domain gap between different RF channels by using adversarial learning to train a more robust model that maintains consistent identification performance despite channel variations. We validate our approach using the UAVSig dataset, comprising real-world over-the-air RF signals from identical drone models operating across several frequency channels, ensuring that the findings correspond to real-world scenarios. The experimental results show CrossRF's efficiency, achieving up to 99.03% accuracy when adapting from Channel 3 to Channel 4, compared to only 26.39% using conventional methods. The model maintains robust performance in more difficult multi-channel scenarios (87.57% accuracy adapting from Channels 1,3 to 2,4) and achieves 89.45% accuracy with 0.9 precision for controller classification. These results confirm CrossRF's ability to significantly reduce performance degradation due to cross-channel variations while maintaining high identification accuracy with minimal training data requirements, making it particularly suitable for practical drone security applications.

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