DiffFace-Edit: A Diffusion-Based Facial Dataset for Forgery-Semantic Driven Deepfake Detection Analysis

• URL: http://arxiv.org/abs/2601.13551v1
• Date: Tue, 20 Jan 2026 03:21:43 GMT
• Title: DiffFace-Edit: A Diffusion-Based Facial Dataset for Forgery-Semantic Driven Deepfake Detection Analysis
• Authors: Feng Ding, Wenhui Yi, Xinan He, Mengyao Xiao, Jianfeng Xu, Jianqiang Du,
• Abstract summary: We introduce the DiffFace-Edit dataset, which contains over two million AI-generated fake images.<n>It features edits across eight facial regions (e.g., eyes, nose) and includes a richer variety of editing combinations.<n>We specifically analyze the impact of detector-evasive samples on detection models.
• Score: 10.354201196086843
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generative models now produce imperceptible, fine-grained manipulated faces, posing significant privacy risks. However, existing AI-generated face datasets generally lack focus on samples with fine-grained regional manipulations. Furthermore, no researchers have yet studied the real impact of splice attacks, which occur between real and manipulated samples, on detectors. We refer to these as detector-evasive samples. Based on this, we introduce the DiffFace-Edit dataset, which has the following advantages: 1) It contains over two million AI-generated fake images. 2) It features edits across eight facial regions (e.g., eyes, nose) and includes a richer variety of editing combinations, such as single-region and multi-region edits. Additionally, we specifically analyze the impact of detector-evasive samples on detection models. We conduct a comprehensive analysis of the dataset and propose a cross-domain evaluation that combines IMDL methods. Dataset will be available at https://github.com/ywh1093/DiffFace-Edit.

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