Multi-Tool Image Editing Attribution in Facial Forgery

2026-09-02

AI Summary

研究解决了多工具面部图像编辑归因问题，通过构建MultiEdit数据集和设计DPEC方法来识别多种编辑工具的使用痕迹。

Abstract

As generative AI tools become increasingly powerful and easy to use, people can easily edit portrait images with a prompt, necessitating the task of image editing attribution, which predicts the involved editing tools from the given image. Existing attribution methods hold the single-tool assumption and can only attribute a specific editing tool, but struggle to handle the more complex and increasingly common multi-tool editing scenarios, where artifacts left by different editing tools are composite and overlapped. To address this gap, we explore Multi-Tool Image Editing Attribution (MIEA), which aims to identify multiple editing tools involved in a multi-tool edited facial image. To simulate the real-life editing operations on facial images, we then construct a new dataset, MultiEdit, which contains 500k+ edited facial images and covers six types of editing tools that support face swapping (Deepfake) and various facial enhancements. Inspired by the findings from data analysis, we design DPEC, a multi-tool attribution method that can capture distinguishable, locality-aware editing tool traces from both spatial and frequency domains with the support of an error-based curriculum learning strategy. Experiments show \Method\ outperforms nine methods for facial images edited in at most five steps.

Problem

• Multi-Tool Image Editing
• Facial Forgery
• Attribution
• Editing Tools
• Composite Artifacts

Innovation

• Multi-Tool Image Editing Attribution
• MultiEdit Dataset
• DPEC Method
• Error-based Curriculum Learning