发表机构
Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences（中国科学院计算技术研究所; 中国科学院大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对多工具面部图像编辑归因难题，构建MultiEdit数据集，设计DPEC方法，在最多5步编辑的面部图像上优于9种现有方法。
AI 中文摘要
随着生成式AI工具日益强大且易于使用，人们可通过提示轻松编辑人像图像，这使得图像编辑归因任务变得必要，该任务旨在从给定图像中预测所使用的编辑工具。现有的归因方法采用单工具假设，仅能归因于特定编辑工具，难以应对更复杂且日益常见的多工具编辑场景，其中不同编辑工具留下的伪影是复合且重叠的。为解决这一差距，我们探索多工具图像编辑归因（Multi-Tool Image Editing Attribution, MIEA），其目标是识别多工具编辑的面部图像中涉及的多种编辑工具。为模拟面部图像的现实编辑操作，我们构建了新数据集MultiEdit，包含50万+张编辑后的面部图像，涵盖支持换脸（Deepfake）的6种编辑工具及各类面部增强工具。受数据分析结果启发，我们设计了DPEC这一多工具归因方法，该方法可借助基于误差的课程学习策略，从空间域和频率域捕获可区分、感知局部性的编辑工具痕迹。实验表明，\nMethod在最多5步编辑的面部图像上，性能优于9种方法。
英文摘要
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.
CommentsAccepted to ACM Multimedia 2026 (MM 2026)
