Source Retrieval for Generative Stylized Images via Destylized Feature Learning

Generative image editing enables users to easily create highly customized and stylized derivatives from reference images, posing new challenges to copyright protection and visual source tracing, particularly in the portrait domain. To address this problem, we formalize the task of Source Retrieval for Stylized Images, which aims to trace a stylized query image back to its exact source image. The main challenge is that existing pretrained visual representations can be sensitive to style variations and generation-induced appearance changes, weakening source discrimination under large style shifts. To enable systematic evaluation of this problem, we build MSIR-Bench, comprising 56K images derived from 5,500 source images and covering 25 artistic styles and five generative editing models. Building on this benchmark, we further present Destylized Feature Learning (DFL), a joint learning framework applicable to various visual backbones that encourages source-discriminative representations with reduced reliance on style-predictive cues by jointly employing a Source Discriminative Loss, a Destylized Triplet Loss, and Style Confusion Regularization. Across seven visual backbones, DFL consistently improves source retrieval over zero-shot and task-adapted baselines in the primary evaluation, while maintaining gains under unseen-source and unseen-style settings and achieving positive average improvements across datasets and generators. Our benchmark and associated resources will be made publicly available at https://anonymous.4open.science/r/Source-Image-Retrieval-8172/.