标题： EditTrack：检测和归因AI辅助的图像编辑 显示英文标题

标题： EditTrack: Detecting and Attributing AI-assisted Image Editing

摘要： 在本工作中，我们提出了并研究了图像编辑检测与归属问题：给定一个基础图像和一个可疑图像，检测旨在确定可疑图像是否是通过AI编辑模型从基础图像中生成的，而归属进一步确定负责的具体编辑模型。现有的检测和归属AI生成图像的方法不足以解决这个问题，因为它们关注的是确定图像是否为AI生成/编辑，而不是是否从特定的基础图像中编辑而来。为了弥补这一差距，我们提出了EditTrack，这是首个针对此图像编辑检测与归属问题的框架。基于对编辑过程的四个关键观察，EditTrack引入了一种新颖的重新编辑策略，并利用精心设计的相似性度量来确定可疑图像是否来源于基础图像，如果是，则确定是由哪个模型生成的。我们在六个数据集上的五种最先进的编辑模型上评估了EditTrack，结果表明它始终能够实现准确的检测和归属，显著优于五种基线方法。 显示英文摘要

摘要： In this work, we formulate and study the problem of image-editing detection and attribution: given a base image and a suspicious image, detection seeks to determine whether the suspicious image was derived from the base image using an AI editing model, while attribution further identifies the specific editing model responsible. Existing methods for detecting and attributing AI-generated images are insufficient for this problem, as they focus on determining whether an image was AI-generated/edited rather than whether it was edited from a particular base image. To bridge this gap, we propose EditTrack, the first framework for this image-editing detection and attribution problem. Building on four key observations about the editing process, EditTrack introduces a novel re-editing strategy and leverages carefully designed similarity metrics to determine whether a suspicious image originates from a base image and, if so, by which model. We evaluate EditTrack on five state-of-the-art editing models across six datasets, demonstrating that it consistently achieves accurate detection and attribution, significantly outperforming five baselines.