EditTrack: 检测并归因AI辅助图像编辑
EditTrack: Detecting and Attributing AI-assisted Image Editing
AI总结：
针对现有AI图像检测方法无法判定图像是否由特定基础图编辑而来的问题，提出首个图像编辑检测归因框架EditTrack，通过重编辑策略与相似性度量实现精准检测归因，在多数据集多模型上显著优于基线。
摘要：
在本研究中，我们提出并研究了图像编辑检测与归因问题：给定一张基础图像和一张可疑图像，检测任务旨在判断可疑图像是否是利用AI编辑模型从基础图像修改而来，而归因任务则进一步识别出具体使用的编辑模型。现有的AI生成图像检测与归因方法无法解决该问题，因为它们聚焦于判断图像是否为AI生成/编辑，而非判断图像是否由某张特定基础图像编辑而来。为填补这一空白，我们提出EditTrack，这是首个面向该图像编辑检测与归因问题的框架。基于对编辑过程的四项关键观察，EditTrack引入了一种新颖的重编辑策略，并利用精心设计的相似性度量指标来判断可疑图像是否源自某张基础图像，若源自则进一步识别所用模型。我们在6个数据集上针对5个最先进的编辑模型对EditTrack进行了评估，结果表明它始终能实现准确的检测与归因效果，性能显著优于5个基线方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
