Semantic Robustness Probing via Inpainting: An Interactive Tool for Safety-Critical Object Detection
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866914606434222080 |
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| author | Steckhan, Nico Prajapati, Krutarth Shao, Weija Vock, Silvia |
| author_facet | Steckhan, Nico Prajapati, Krutarth Shao, Weija Vock, Silvia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_27155 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Semantic Robustness Probing via Inpainting: An Interactive Tool for Safety-Critical Object Detection Steckhan, Nico Prajapati, Krutarth Shao, Weija Vock, Silvia Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Semantic Robustness Probing via Inpainting: An Interactive Tool for Safety-Critical Object Detection |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2605.27155 |