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

TL;DR

SpoofTrackBench is a comprehensive, reproducible benchmark framework for evaluating the robustness of real-time UAV tracking systems against radar spoofing attacks, using simulated scenarios and interpretability tools.

Contribution

It introduces a modular, open benchmark with visualization and analysis tools for spoof-aware UAV tracking, leveraging a new dataset and simulation of various spoofing attacks.

Findings

Effective separation of clean and spoofed detection streams

Visualization of spoof-induced trajectory divergence

Quantitative metrics for spoofing attack impact

Abstract

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.