电波科学学报2026，Vol.41Issue(3)：562-572,11.DOI:10.12265/j.cjors.2025002
基于混合异常暴露的开集射频指纹识别
Open-set RF fingerprint identification based on mixed outlier exposure
摘要
Abstract
Radio frequency fingerprinting(RFF)identification,a crucial technology in physical layer security,enables device authentication through the analysis of hardware impairment characteristics embedded within signals.To address the semantic shift challenge posed by unknown radiation sources in open environments,this paper presents CutOE,a novel framework for open-set RFF identification that implements open-set recognition through both data-centric and loss function approaches.At the data level,At the data level,considering the specific characteristics of electromagnetic data,unlabeled anomaly samples are introduced during the training phase,and a random cut-smooth technique is employed to generate virtual anomaly samples,effectively expanding the prior knowledge of the open space.At the loss function level,the framework introduces soft labels and Jensen-Shannon divergence loss to constrain the model's confidence decay smoothly,while implementing efficient open-set recognition based on confidence thresholds.Extensive experiments conducted on Wi-Fi datasets demonstrate that under high signal-to-noise ratio conditions,the CutOE framework achieves F1 scores of 93.57%and 72.20%at openness ratios of 0.105 57 and 0.396 98,respectively.Furthermore,ablation studies validate the effectiveness and scalability of each framework component,with random cutting strategies and Jensen-Shannon divergence significantly enhancing open-set recognition performance.关键词
物理层安全/射频指纹(RFF)/开集识别(OSR)/混合异常暴露Key words
physical layer security/RF fingerprint(RFF)/open set recognition(OSR)/mixed outlier exposure分类
信息技术与安全科学引用本文复制引用
彭进先,赵超,桑苗苗,周伦,秦剑琪,龙照飞..基于混合异常暴露的开集射频指纹识别[J].电波科学学报,2026,41(3):562-572,11.基金项目
国防预研项目(2023XXXX007)National Defense Pre-research Project(2023XXXX007) （2023XXXX007）
