HoRFFI: High-Openness RF Fingerprint Identification with a Similarity-Enhanced Variational Information Bottleneck

Computer Science > Cryptography and Security [Submitted on 5 Aug 2026] HoRFFI: High-Openness RF Fingerprint Identification with a Similarity-Enhanced Variational Information Bottleneck Shuiguang Zeng (1,2), Yuxiang Shen (1,2), Yuanyu Zhang (3), Yulong Shen (3), Zhiyuan Tan (4), Houbing Herbert Song (5) ((1) College of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China, (2) Hebei Key Laboratory of Network and Information Security, Shijiazhuang, China, (3) School of Computer Science and Technology, Xidian University, Xi'an, China, (4) School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, U.K., (5) Department of Information Systems, University of Maryland, Baltimore County (UMBC), Baltimore, USA) 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. Comments: Submitted to IEEE INFOCOM 2026 Subjects: Cryptography and Security (cs.CR) Cite as: arXiv:2608.04881 [cs.CR]   (or arXiv:2608.04881v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2608.04881 Focus to learn more Submission history From: Yuxiang Shen [view email] [v1] Wed, 5 Aug 2026 14:07:52 UTC (1,220 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)