Open-Set RF Fingerprint Identification with Synthetic Feature Constraint

The rapid expansion of the Internet of Things (IoT) has heightened the necessity for device identity authentication to ensure security. Radio frequency fingerprint identification (RFFI) has emerged as a promising solution for this purpose, which leverage unique signal distortions caused by hardware impairments to authenticate device identities. However, most RFFI methods operate under a closed-set assumption and usually mistakenly identify unknown devices from the open set as known devices. In this paper, we propose a Synthetic Feature Constrain for open-set Recognition (SFCR) method to maintain classification performance on known devices and identify unknowns. Specifically, we modify the nonlinear characteristics of known devices based on the power amplifier nonlinearity model of radio frequency fingerprints (RFF) to synthesize signals for unknown devices. Furthermore, we propose a synthetic feature constraint to calibrate the position of synthetic devices in the feature space, such that they lie between the feature centers of the collective synthetic and originating known devices. As synthetic devices represent only a subset of the real unknown devices, we also introduce a calibration method for the prediction results. Experiments on a publicly available LoRa device dataset have validated the effectiveness of our approach. The code is released on github.com/wzyxwqx/SFCR.