Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio-frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed-set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample's features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices.

翻译：射频指纹识别是一种轻量级的物理层身份认证技术，它通过分析由不可避免的微小硬件损伤引起的信号特征差异来识别射频设备。然而，基于闭集识别的现有RFFI方法难以在开放环境中检测未知的未授权设备。此外，合法设备间的特征干扰会进一步损害识别精度。本文提出了一种用于开放集识别的联合射频指纹预测与孪生比较框架。具体而言，我们首先采用一个射频指纹预测网络来预测最可能的类别结果，随后在孪生网络中对测试样本特征与已注册样本进行详细比较。所提出的JRFFP-SC框架消除了类间干扰，并有效应对了开放集识别相关的挑战。仿真结果表明，我们提出的JRFFP-SC框架能够实现优异的恶意设备检测能力以及对设备分类的泛化能力。