AI 中文总结
该研究针对WiFi射频指纹识别的开放集问题，提出C²T-OpenMax框架，通过中心约束学习与置信度引导尾部建模优化性能，在公开数据集上优于所有基线方法。
AI 中文摘要
射频指纹识别（RFF）可通过发射机特有的硬件缺陷实现设备认证，但实际部署需要跨环境的开放集识别。数据增强可提升环境泛化能力，但可能产生分散、低置信度的已知类表示，从而扭曲OpenMax所用的类统计量。为解决该问题，我们提出C²T-OpenMax，这是一种结合中心约束学习与置信度引导尾部建模的增强OpenMax框架。前者提升类内紧凑性，使逐类表示更适合基于距离的建模；后者仅保留正确分类的高置信度logits用于平均激活向量估计和Weibull拟合，减少模糊边界样本带来的偏差。两个模块协同优化表示几何与OpenMax构建，同时保留数据增强的优势。在公开WiFi CSI数据集上的实验表明，C²T-OpenMax在8个位置组中的7个达到最高开放集准确率，且在所有测试的开放度水平下，其受试者工作特征曲线下面积（AUROC）和开放集分类率（OSCR）均优于所有基线方法；在最大开放度设置下，与增强OpenMax基线相比，其准确率提升12.31%，AUROC提升0.0887，OSCR提升0.0856。
英文摘要
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.
