2026/05/26 by Nico Steckhan, Krutarth Prajapati, Weija Shao +1 · 1 voice
Computer Science · #Advanced Neural Network Applications #Adversarial Robustness in Machine Learning #Detector #Inference #Object detection #Robustness (evolution) #Software Testing and Debugging Techniques #Software deployment #Upload #cs.AI #cs.CV
paper · pdf · open access · doi:10.48550/arxiv.2605.27155
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2026/05/26 · arxiv published 2026/05/26 · arxiv updated 2026/05/27 · openalex created_date 2026/05/28 · openalex updated_date 2026/07/28
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 SemProbe on hand detection for dimension saws, targeting factors from insurance-oriented test criteria.