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.

翻译：生成模型如今能够生成难以察觉、细粒度的人脸篡改图像，这带来了重大的隐私风险。然而，现有的人工智能生成人脸数据集普遍缺乏对具有细粒度局部篡改样本的关注。此外，尚未有研究者深入探究发生在真实样本与篡改样本之间的拼接攻击对检测器的实际影响。我们将此类样本称为检测器规避样本。基于此，我们引入了DiffFace-Edit数据集，该数据集具有以下优势：1）它包含超过两百万张人工智能生成的伪造图像。2）它涵盖了八个面部区域（例如，眼睛、鼻子）的编辑，并包含了更丰富的编辑组合，如单区域编辑和多区域编辑。此外，我们专门分析了检测器规避样本对检测模型的影响。我们对数据集进行了全面分析，并提出了一种结合IMDL方法的跨域评估方案。数据集将在 https://github.com/ywh1093/DiffFace-Edit 提供。