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

• URL: http://arxiv.org/abs/2602.05577v1
• Date: Thu, 05 Feb 2026 12:01:09 GMT
• Title: LocateEdit-Bench: A Benchmark for Instruction-Based Editing Localization
• Authors: Shiyu Wu, Shuyan Li, Jing Li, Jing Liu, Yequan Wang,
• Abstract summary: We propose a large-scale dataset comprising $231$K edited images to benchmark forgery localization methods.<n>Our dataset incorporates four cutting-edge editing models and covers three common edit types.<n>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.
• Score: 21.62979058692505
• License: http://creativecommons.org/licenses/by/4.0/
• 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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