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This paper introduces C$^2$T-OpenMax, a novel framework for open-set WiFi RF fingerprinting that integrates center-constrained learning with confidence-guided tail modeling to enhance device authentication across varying environments. By improving intra-class compactness and focusing on high-confidence logits, the method effectively mitigates the distortion of class statistics caused by data augmentation. Experimental results demonstrate that C$^2$T-OpenMax significantly outperforms existing methods, achieving superior open-set accuracy and area under the ROC curve across multiple environments.
Achieving a 12.31% accuracy boost in open-set WiFi RF fingerprinting, C$^2$T-OpenMax redefines class representation geometry for better device authentication.
Radio frequency fingerprinting (RFF) enables device authentication from transmitter-specific hardware imperfections, but practical deployment requires cross-environment open-set recognition. Data augmentation improves environmental generalization, yet may yield dispersed, low-confidence known-class representations that distort the class statistics used by OpenMax. To address this problem, we propose C$^2$T-OpenMax, an enhanced OpenMax framework combining center-constrained learning with confidence-guided tail modeling. The former improves intra-class compactness, making class-wise representations more suitable for distance-based modeling. The latter retains only correctly classified, high-confidence logits for mean activation vector estimation and Weibull fitting, reducing bias from ambiguous boundary samples. Together, the two modules refine representation geometry and OpenMax construction while preserving augmentation benefits. Experiments on a public WiFi CSI dataset show that C$^2$T-OpenMax achieves the highest open-set accuracy in seven of eight location groups and outperforms all baselines in area under the receiver operating characteristic curve (AUROC) and open-set classification rate (OSCR) across every tested openness level. Under the largest-openness setting, it improves accuracy by 12.31%, AUROC by 0.0887, and OSCR by 0.0856 over the augmented OpenMax baseline.
