Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting

• URL: http://arxiv.org/abs/2607.09760v2
• Date: Tue, 14 Jul 2026 11:55:07 GMT
• Title: Physics-Informed Structure Anchoring With Capture-Aware Prototype Calibration for Cross-Environment RF Fingerprinting
• Abstract summary: Radio frequency fingerprint identification (RFFI) exploits transmitter-specific hardware imperfections for Internet of Things (IoT) devices.<n>This article proposes physicsinformed structure anchoring with capture-aware prototype calibration (PISA-CAPC) to address both representation and decision mismatches.
• Score: 9.281236432794417
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radio frequency fingerprint identification (RFFI) exploits transmitter-specific hardware imperfections as physicallayer identity cues for Internet of Things (IoT) devices, but deep models often degrade across acquisition environments. In multi-antenna reception, antenna topology and frequencyoffset dynamics structure receiver observations, while capturedependent variation distorts target embeddings and misaligns source-trained decision boundaries. This article proposes physicsinformed structure anchoring with capture-aware prototype calibration (PISA-CAPC) to address both representation and decision mismatches. The two stages separate source representation construction from target decision correction. During source training, PISA organizes antenna tokens through a topology-guided graph, conditions propagation on CFO-derived acquisition dynamics, and applies bounded contextual residual suppression to preserve identity evidence. At deployment, unlabeled capture-aware prototype calibration (U-CAPC) estimates capture-local prototypes and recalibrates target decision scores while keeping the representation and source classifier fixed. Thus, calibration uses neither target labels nor target-domain backbone updates. On a measured WiFi benchmark with four receive antennas and ten transmitters, PISA-CAPC achieves a mean target-domain Macro-F1 of 0.9257 under a balanced transductive setting. Component ablations support complementary roles for topology-guided anchoring, CFO-conditioned modulation, reliability-aware token aggregation, contextual suppression, and capture-aware calibration. These results indicate that physically motivated representation learning can be combined with labelfree decision calibration to improve cross-environment RFFI under the evaluated protocol without changing the deployed backbone.

Related papers

• Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
  This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
  arXiv  Detail & Related papers  (2026-07-29T05:16:59Z)
• DKDNet: Dual Knowledge and Data-Driven Network for Cross-Domain Automatic Modulation Classification [45.220843998540545]
  We consider signal prior knowledge, grounded in communication protocols and physical principles, as a potential way to enhance cross-domain representation learning.<n>This paper first analyzes five commonly adopted signal representations that instantiate different signal priors.<n>Based on that, a dual knowledge and data-driven network (DKDNet) is proposed for cross-domain AMC.
  arXiv  Detail & Related papers  (2026-07-09T01:14:42Z)
• Hierarchical Consistency Learning for Test-time Adaptation in Camouflage Perception [50.278200968044665]
  Camouflaged object detection (COD) aims to localize targets that exhibit minimal perceptual differences from backgrounds through physical attributes.<n>Existing methods, constrained by the static train-then-freeze paradigm, suffer from domain rigidity and dependency annotation.<n>We propose the hierarchical consistency learning framework, which integrates test-time adaptation for dynamic representation recalibration.
  arXiv  Detail & Related papers  (2026-05-25T09:57:46Z)
• Rethinking Feature Alignment in Generalist Graph Anomaly Detection: A Relational Fingerprint-based Approach [60.199985286210904]
  Generalist graph anomaly detection (GAD) aims to detect anomalies on unseen graphs without graph-specific retraining.<n>Existing approaches primarily focus on aligning heterogeneous data across domains, which ignores different feature semantics.<n>We propose Fingerprint-based generalist GAD approach (ReFi-GAD)<n>We show that ReFi-GAD significantly outperforms state-of-the-art methods in experiments on 14 datasets.
  arXiv  Detail & Related papers  (2026-05-25T05:12:26Z)
• MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation [65.068801413044]
  Continual test-time adaptation adapts a source-pretrained model to non-stationary, unlabeled target streams.<n>We introduce MoASE, a mixture-of-experts that disentangles domain-agnostic structure from domain-specific texture.
  arXiv  Detail & Related papers  (2026-05-18T01:52:12Z)
• TIP: Resisting Gradient Inversion via Targeted Interpretable Perturbation in Federated Learning [8.156452885913108]
  Federated Learning (FL) facilitates collaborative model training while preserving data locality.<n>The exchange of gradients renders the system vulnerable to Gradient Inversion Attacks (GIAs)<n>We propose Targeted Interpretable Perturbation (TIP), a novel defense framework that integrates model interpretability with frequency domain analysis.
  arXiv  Detail & Related papers  (2026-02-12T06:32:49Z)
• Attention-Enhanced Graph Filtering for False Data Injection Attack Detection and Localization [0.0]
  False data injection attacks (FDIAs) can compromise measurement integrity and threaten reliable system operation.<n>This paper proposes a joint FDIA detection and localization framework that integrates auto-regressive moving average (ARMA) graph filters with an Transformer architecture.<n>The proposed method is evaluated using real-world load data from the New York Independent System Operator (NYISO) applied to the IEEE 14- and 300-bus systems.
  arXiv  Detail & Related papers  (2026-01-26T21:33:47Z)
• Exploiting Radio Frequency Fingerprints for Device Identification: Tackling Cross-receiver Challenges in the Source-data-free Scenario [17.211137756661955]
  We present a source-data-free cross-receiver RFFI problem, where a model pretrained on labeled signals from a source receiver must adapt to unlabeled signals from a target receiver.<n>We propose Momentum Soft pseudo-label Source Hypothesis Transfer (MS-SHOT), a new method for SCRFFI that incorporates momentum-center-guided soft pseudo-labeling and enforces global structural constraints.<n>MS-SHOT consistently outperforms existing approaches in both accuracy and robustness, offering a practical and scalable solution for source-data-free cross-receiver adaptation in RFFI.
  arXiv  Detail & Related papers  (2025-12-18T15:20:33Z)
• Uncertainty-Guided Selective Adaptation Enables Cross-Platform Predictive Fluorescence Microscopy [65.15943255667733]
  We introduce Subnetwork Image Translation ADDA with automatic depth selection (SIT-ADDA-Auto)<n>We show that adapting only the earliest convolutional layers, while freezing deeper layers, yields reliable transfer.<n>Our results provide a design rule for label-free adaptation in microscopy and a recipe for field settings; the code is publicly available.
  arXiv  Detail & Related papers  (2025-11-15T03:01:05Z)
• Source-Free Object Detection with Detection Transformer [59.33653163035064]
  Source-Free Object Detection (SFOD) enables knowledge transfer from a source domain to an unsupervised target domain for object detection without access to source data.<n>Most existing SFOD approaches are either confined to conventional object detection (OD) models like Faster R-CNN or designed as general solutions without tailored adaptations for novel OD architectures, especially Detection Transformer (DETR)<n>In this paper, we introduce Feature Reweighting ANd Contrastive Learning NetworK (FRANCK), a novel SFOD framework specifically designed to perform query-centric feature enhancement for DETRs.
  arXiv  Detail & Related papers  (2025-10-13T07:35:04Z)
• You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment [45.62136459502005]
  We propose a network to perform full reference (FR) and no reference (NR) IQA. We first employ an encoder to extract multi-level features from input images. A Hierarchical Attention (HA) module is proposed as a universal adapter for both FR and NR inputs. A Semantic Distortion Aware (SDA) module is proposed to examine feature correlations between shallow and deep layers of the encoder.
  arXiv  Detail & Related papers  (2023-10-14T11:03:04Z)
• Attentive Prototypes for Source-free Unsupervised Domain Adaptive 3D Object Detection [85.11649974840758]
  3D object detection networks tend to be biased towards the data they are trained on. We propose a single-frame approach for source-free, unsupervised domain adaptation of lidar-based 3D object detectors.
  arXiv  Detail & Related papers  (2021-11-30T18:42:42Z)
• ACDC: Online Unsupervised Cross-Domain Adaptation [15.72925931271688]
  We propose ACDC, an adversarial unsupervised domain adaptation framework. ACDC encapsulates three modules into a single model: A denoising autoencoder that extracts features, an adversarial module that performs domain conversion, and an estimator that learns the source stream and predicts the target stream. Our experimental results under the prequential test-then-train protocol indicate an improvement in target accuracy over the baseline methods, achieving more than a 10% increase in some cases.
  arXiv  Detail & Related papers  (2021-10-04T11:08:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.