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arXiv · 2602.05577

LocateEdit-Bench: A Benchmark for Instruction-Based Editing Localization

Shiyu Wu·Shuyan Li·Jing Li·Jing Liu·Yequan Wang
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Abstract

Recent advancements in image editing have enabled highly controllable and semantically-aware alteration of visual content, posing unprecedented challenges to manipulation localization. However, existing AI-generated forgery localization methods primarily focus on inpainting-based manipulations, making them ineffective against the latest instruction-based editing paradigms. To bridge this critical gap, we propose LocateEdit-Bench, a large-scale dataset comprising $231$K edited images, designed specifically to benchmark localization methods against instruction-driven image editing. Our dataset incorporates four cutting-edge editing models and covers three common edit types. We conduct a detailed analysis of the dataset and develop two multi-metric evaluation protocols to assess existing localization methods. Our work establishes a foundation to keep pace with the evolving landscape of image editing, thereby facilitating the development of effective methods for future forgery localization. Dataset will be open-sourced upon acceptance.

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Shiyu Wu, Shuyan Li, Jing Li, Jing Liu, Yequan Wang. 2026-02-05. LocateEdit-Bench: A Benchmark for Instruction-Based Editing Localization. https://arxiv.org/abs/2602.05577

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