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

arXiv:2608.04881 · cs.CR · Submitted 2026-08-05 · Read on arXiv

Shuiguang Zeng, Yuxiang Shen, Yuanyu Zhang, Yulong Shen, Zhiyuan Tan, Houbing Herbert Song

Hebei Key Laboratory of Network and Information Security, Shijiazhuang, China · Hebei Key Laboratory of Network and Information Security, Shijiazhuang, China · School of Computer Science and Technology, Xidian University, Xi'an, China · School of Computer Science and Technology, Xidian University, Xi'an, China · School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, U.K · Department of Information Systems, University of Maryland, Baltimore County

cs.CR

Submitted: 2026-08-05

Comments: Submitted to IEEE INFOCOM 2026

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 15× in this paper · explained in 1 episode
• Clustering — 14× in this paper · explained in 2 episodes
• Information Bottleneck — 13× in this paper · explained in 3 episodes
• AUC — 10× in this paper · explained in 2 episodes
• Representation Learning — 8× in this paper · explained in 3 episodes
• Embeddings — 7× in this paper · explained in 2 episodes
• Generalization — 7× in this paper · explained in 14 episodes
• Fingerprinting — 6× in this paper · explained in 2 episodes
• Augmentation — 5× in this paper · explained in 2 episodes
• Evaluation Protocol — 4× in this paper · explained in 2 episodes
• Pseudo-labels — 4× in this paper · explained in 2 episodes
• Training Objective — 4× in this paper · explained in 3 episodes
• Contrastive Learning — 3× in this paper · explained in 9 episodes
• Meta-learning — 3× in this paper · explained in 3 episodes
• Mutual Information — 3× in this paper · explained in 2 episodes
• Regularization — 3× in this paper · explained in 3 episodes
• Transferability — 3× in this paper · explained in 5 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Loss Function — 2× in this paper · explained in 2 episodes
• Training Objectives — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 3 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Convolutional Neural Networks (CNNs) — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Dimensionality Reduction — 1× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Knowledge Graph — 1× in this paper · explained in 8 episodes
• Knowledge graphs — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Open-Set Recognition (OSR) — 1× in this paper · explained in 2 episodes
• Overfitting — 1× in this paper · explained in 2 episodes
• Semi-supervised Learning — 1× in this paper · explained in 4 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformers — 1× in this paper · explained in 2 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Open-world Semi-supervised Novel Class Discovery

Related papers

• SoK: AI-Augmented Binary Reversing
• Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
• Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
• Estimating the Social Cost of Corporate Data Breaches
• Efficient Fuzzy PSI under One-Sided Assumptions
• Sealing the Audit-Runtime Gap for LLM Skills