COCO-Inpaint Benchmark Targets Inpainting-Based Image Manipulation Detection
Researchers have introduced COCO-Inpaint, a benchmark designed to detect and localize inpainting-based image manipulations. While existing Image Manipulation Detection and Localization (IMDL) methods focus on splicing or copy-move forgeries, inpainting benchmarks are scarce. COCO-Inpaint offers three key contributions: 238,302 high-quality inpainted images generated by six state-of-the-art inpainting models, four mask generation strategies with optional text guidance for diverse scenarios, and large-scale semantic diversity. The benchmark highlights intrinsic inconsistencies between inpainted and authentic regions, aiming to advance multimedia authenticity and security.
Key facts
- COCO-Inpaint is a benchmark for detecting and localizing inpainting-based image manipulations.
- It addresses the gap in IMDL methods that mainly target splicing or copy-move forgeries.
- The benchmark includes 238,302 inpainted images generated by six state-of-the-art inpainting models.
- Four mask generation strategies with optional text guidance enable diverse generation scenarios.
- The dataset offers large-scale coverage with rich semantic diversity.
- It highlights intrinsic inconsistencies between inpainted and authentic regions.
- The work is published on arXiv with ID 2504.18361.
- The announcement type is replace-cross.
Entities
Institutions
- arXiv