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Semantic Robustness Probing via Inpainting: An Interactive Tool for Safety-Critical Object Detection
Nico Steckha · 2026-05-27 · via cs.AI updates on arXiv.org

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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.27155 [cs.CV]
  (or arXiv:2605.27155v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2605.27155

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Nico Steckhan [view email]
[v1] Tue, 26 May 2026 15:15:38 UTC (1,099 KB)