AI 中文总结
针对高开放度RFFI问题，提出仅用少量带标签训练设备的HoRFFI框架，采用SVIB监督机制，在LoRa和Wi-Fi数据集上实现新类准确率与AUC的显著提升。
AI 中文摘要
射频指纹识别（RFFI）是一种极具应用前景的无线设备认证技术。然而，实际的RFFI系统必须在仅用少量带标签基础设备类训练特征提取器的情况下，完成新授权设备的注册并拒绝此前未见过的设备，由此产生了高开放度RFFI问题。现有的开放集识别方法通常依赖从大量多样的已知设备类中学习的特征空间，这限制了它们在实际场景中的适用性。为应对这一挑战，我们提出了HoRFFI，一种高开放度RFFI框架，仅使用少量带标签训练设备即可支持可扩展的设备识别和未知设备拒绝。HoRFFI采用基于相似度增强变分信息瓶颈（SVIB）的监督机制，该机制可降低编码器对训练类多样性的依赖，并学习更具可迁移性的嵌入空间。此监督机制利用特征空间增强和聚类来推导样本间相似度信息，为嵌入空间的正则化提供补充监督。在公开的LoRa和Wi-Fi数据集上进行的实验表明，与表现最佳的基线方法相比，HoRFFI在新类准确率上分别实现了0.112和0.288的绝对提升，对应的AUC分别提升了0.029和0.060。
英文摘要
Radio frequency fingerprint identification (RFFI) is a promising technique for wireless device authentication. However, practical RFFI systems must enroll newly authorized devices while rejecting previously unseen ones, even when the feature extractor is trained on only a few labeled base-device classes, giving rise to a high-openness RFFI problem. Existing open-set recognition methods typically rely on feature spaces learned from a large and diverse set of known-device classes, limiting their applicability in practical scenarios. To address this challenge, we propose HoRFFI, a high-openness RFFI framework that supports scalable device identification and unknown-device rejection using only a small number of labeled training devices. HoRFFI employs a similarity-enhanced variational information bottleneck (SVIB)-based supervision mechanism, which reduces the encoder's dependence on training-class diversity and learns a more transferable embedding space. This supervision mechanism uses feature-space augmentation and clustering to derive inter-sample similarity information, which provides supplementary supervision for regularizing the embedding space. Experiments on public LoRa and Wi-Fi datasets show that HoRFFI achieves absolute improvements of \(0.112\) and \(0.288\) in novel-class accuracy, respectively, and corresponding absolute AUC improvements of \(0.029\) and \(0.060\) over the best-performing baselines.
CommentsSubmitted to IEEE INFOCOM 2026
