Testing object detectors in safety-critical domains requires semantically meaningful probes beyond pixel-level corruptions. We present SemProbe, a tool for semantic robustness probing: users upload deployment images, create masks manually or automatically, select operational design domain-derived factors (or custom prompts), and run diffusion-based controlled inpainting. The system supports batch jobs, parallel seed/workflow variations, and configurable generation parameters. After each output, model inference runs automatically and displays annotated before/after comparisons with performance deltas. All probes are logged as structured artifacts, enabling traceable robustness evidence aligned with safety evaluation workflows. We demonstrate \textsc{SemProbe} on hand detection for dimension saws, targeting factors from insurance-oriented test criteria.
@article{arxiv.2605.27155,
title = {Semantic Robustness Probing via Inpainting: An Interactive Tool for Safety-Critical Object Detection},
author = {Nico Steckhan and Krutarth Prajapati and Weija Shao and Silvia Vock},
journal= {arXiv preprint arXiv:2605.27155},
year = {2026}
}