SinFormer: A Tailored Transformer for Robust Radio Frequency Fingerprint Identification

2026-05-23

AI Summary

This work addresses the vulnerability of wireless and Internet-of-Things (IoT) devices to IP/MAC address spoofing attacks and the consequent difficulty in achieving secure and reliable identification in complex radio-frequency (RF) environments. To tackle this challenge, the authors propose SinFormer, a deep learning–based RF fingerprinting framework that introduces a novel multi-scale self-attention mechanism tailored for RF signals. This architecture effectively captures both coarse- and fine-grained fingerprint features, while a two-stage training strategy enhances robustness under adverse conditions such as low signal-to-noise ratios and dynamic channel variations. Experimental results on real-world datasets demonstrate that SinFormer consistently outperforms existing methods across diverse challenging scenarios, achieving significant improvements in both identification accuracy and stability.

Abstract

With the rapid proliferation of wireless and Internet of Things (IoT) devices, ensuring secure and reliable device identification has become a significant challenge. Traditional security techniques, such as IP or MAC address-based authentication, are susceptible to spoofing, whereas Radio Frequency Fingerprint Identification (RFFI) offers a more secure alternative by exploiting the unique hardware imperfections in devices' RF signals. In this paper, we propose a novel deep learning-based framework for RFFI that enhances both accuracy and reliability in challenging RF environments. The core of our approach is the Signal Inception Transformer (SinFormer), which leverages a specialized multi-scale self-attention mechanism to effectively capture both large-scale and fine-grained fingerprints in signals, significantly improving identification accuracy. To further enhance robustness and reliability, we introduce a two-stage training strategy that enables the model to learn general signal features and maintain performance under adverse conditions, such as low Signal-to-Noise Ratio (SNR) or channel variations. The effectiveness of the proposed method is validated using a real-world dataset. Experimental results show that the SinFormer framework consistently outperforms existing methods in accuracy and robustness across diverse and challenging scenarios.

Problem

• Radio Frequency Fingerprint Identification
• device identification
• robustness
• spoofing resistance
• IoT security

Innovation

• Radio Frequency Fingerprint Identification
• Transformer
• Multi-scale Self-attention
• Two-stage Training
• Robustness