EditScan: Towards Generalizable Detection and Localization of Instruction-Driven Image Editing

Instruction-driven image editing models can realistically alter local image regions, posing severe challenges to the accuracy and generalization of image editing detection and localization. Existing forensic benchmarks for generative local editing either cover a limited range of instruction-driven editors or are primarily designed around mask-conditioned inpainting,leaving the generalization of forensic methods to rapidly evolving instruction-driven editing models insufficiently evaluated. To address this gap, we introduce EditScan-Bench together with a strong baseline approach, EditScan. EditScan-Bench is a purpose-built benchmark for instruction-driven image editing forensics, comprising 44K annotated image pairs at 1K resolution, with machine-produced masks that are carefully validated by human experts. It spans 3 widely used open-weight and 5 proprietary model families across four common manipulation types: Add, Remove, Replace, and Attribute Change. Building upon EditScan-Bench, we further propose EditScan, which connects vision foundation models with general-purpose segmentation models through specialized adaptation modules to enable both image-level detection and pixel-level localization. EditScan includes two complementary designs: (1) explicitly capturing local patch evidence to increase sensitivity to subtle edits, improving image-level detection; and (2) converting the foundation model’s global-local features into forensic prompts that steer the segmentation model toward accurate manipulation localization. In cross-generator tests, EditScan raises average image-level detection F1 and Accuracy by $0.12$ ($0.64\to 0.76$) and $0.08$ ($0.74\to 0.82$), respectively, compared with the strongest baseline. For pixel-level localization, it achieves improvements of $0.22$ F1 ($0.29\to 0.51$) and $0.21$ IoU ($0.26\to 0.47$), while also showing stronger robustness to post-processing degradations. The benchmark dataset and code are available at https://anonymous.4open.science/r/EditScan.