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

arXiv: https://arxiv.org/abs/2605.27155 · HTML: https://mlsanity.com/paper/2605.27155/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 4 of 4)

- [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://mlsanity.com/paper/1907.07484/) (2019)
- [COCO-O: A Benchmark for Object Detectors under Natural Distribution Shifts](https://mlsanity.com/paper/2307.12730/) (2023)
- [ODGEN: Domain-specific Object Detection Data Generation with Diffusion Models](https://mlsanity.com/paper/2405.15199/) (2024)
- [DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generation](https://mlsanity.com/paper/2503.11122/) (2025)

## Built on by (most cited first, 1 of 1)

- [Train, Test, Re-evaluate: Schedule-Sensitive Evaluation of Generative Data for Hand Detection](https://mlsanity.com/paper/2606.01896/) (2026)

## Abstract (arXiv)

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 textscSemProbe on hand detection for dimension saws, targeting factors from insurance-oriented test criteria.