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Semantic Robustness Probing via Inpainting: An Interactive Tool for Safety-Critical Object Detection

2026-05-26 15:15:38
Nico Steckhan, Krutarth Prajapati, Weija Shao, Silvia Vock
arXiv_AI
           
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.

Abstract (translated)

URL

https://arxiv.org/abs/2605.27155

PDF

https://arxiv.org/pdf/2605.27155.pdf

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