# SpoofTrackBench: Interpretable AI for Spoof-Aware UAV Tracking and Benchmarking

arXiv: https://arxiv.org/abs/2510.22726 · HTML: https://mlsanity.com/paper/2510.22726/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [Hybrid Quantum-Classical Encoding for Accurate Residue-Level pKa Prediction](https://mlsanity.com/paper/2603.11061/) (2026)

## Abstract (arXiv)

SpoofTrackBench is a reproducible, modular benchmark for evaluating adversarial robustness in real-time localization and tracking (RTLS) systems under radar spoofing. Leveraging the Hampton University Skyler Radar Sensor dataset, we simulate drift, ghost, and mirror-type spoofing attacks and evaluate tracker performance using both Joint Probabilistic Data Association (JPDA) and Global Nearest Neighbor (GNN) architectures. Our framework separates clean and spoofed detection streams, visualizes spoof-induced trajectory divergence, and quantifies assignment errors via direct drift-from-truth metrics. Clustering overlays, injection-aware timelines, and scenario-adaptive visualizations enable interpretability across spoof types and configurations. Evaluation figures and logs are auto-exported for reproducible comparison. SpoofTrackBench sets a new standard for open, ethical benchmarking of spoof-aware tracking pipelines, enabling rigorous cross-architecture analysis and community validation.