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.

翻译：近年来，图像编辑技术的进步使得对视觉内容进行高度可控且语义感知的修改成为可能，这为篡改定位带来了前所未有的挑战。然而，现有的AI生成伪造定位方法主要关注基于修复的篡改操作，使其在面对最新的基于指令的编辑范式时效果不佳。为弥补这一关键空白，我们提出了LocateEdit-Bench，这是一个包含23.1万张编辑图像的大规模数据集，专门用于针对指令驱动的图像编辑对定位方法进行基准测试。我们的数据集整合了四种前沿编辑模型，并涵盖三种常见的编辑类型。我们对数据集进行了详细分析，并制定了两种多指标评估方案来评估现有的定位方法。本研究为跟上图像编辑技术的演进步伐奠定了基础，从而推动未来伪造定位有效方法的发展。数据集将在论文录用后开源。