检测图像是否被特定AI模型修改，并识别具体使用了哪个模型。
EditTrack: Detecting and Attributing AI-assisted Image Editing
- 通过重编辑策略和相似性度量，判断可疑图是否源自基图。
- 在六个数据集上对五种主流编辑模型测试，准确率显著优于基线。
- 适合需要溯源图像篡改或验证内容真实性的安全与媒体领域。
本文提出并研究图像编辑检测与归属问题：给定一张基础图像和一张可疑图像，检测任务判断可疑图像是否由基础图像经由AI编辑模型生成，而归属任务进一步确定具体使用的编辑模型。现有方法主要针对图像是否为AI生成/编辑进行判断，无法有效解决从特定基图衍生的问题。为此，我们提出EditTrack，首个针对此问题的框架。基于对编辑过程的四项关键观察，EditTrack引入新颖的重编辑策略，并设计精细的相似性度量，以判断可疑图像是否源自基图及具体编辑模型。我们在六大数据集上评估了五种前沿编辑模型，结果表明EditTrack在检测与归属任务中均表现优异，显著超越五个基线方法。
原文摘要 · Abstract (English)
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.
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