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.

翻译：随着无线及物联网（IoT）设备的快速普及，确保安全可靠的设备识别已成为一项重大挑战。传统的安全技术（如基于IP或MAC地址的身份验证）易受欺骗，而射频指纹识别（RFFI）通过利用设备射频信号中独特的硬件缺陷，提供了一种更安全的替代方案。本文提出了一种基于深度学习的新型RFFI框架，可提升复杂射频环境下的准确性与可靠性。该框架的核心是信号初始Transformer（SinFormer），其利用专门设计的的多尺度自注意力机制，有效捕获信号中的大规模与细粒度指纹，显著提高识别精度。为进一步增强鲁棒性与可靠性，我们引入了一种两阶段训练策略，使模型能够学习通用信号特征，并在低信噪比（SNR）或信道变化等恶劣条件下保持性能。通过真实数据集验证了所提方法的有效性。实验结果表明，SinFormer框架在多种复杂场景下的准确性与鲁棒性均持续优于现有方法。