mlsanityData to 14 Oct 2026

Multi-Tool Image Editing Attribution in Facial Forgery

arXiv 2609.02751 ↗First posted 2 Sep 2026cs.CV cs.MMcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 30 of its 56 references that are in the map. First posted on arXiv 2 Sep 2026.

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References · 30

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18 more
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