scaledf/ScaleDF

ScaleDF是目前深度伪造检测领域最大的数据集，包含来自51个不同数据集（域）的超过580万张真实图像和由102种深度伪造方法生成的超过880万张伪造图像。使用ScaleDF，研究人员可以观察到类似于大型语言模型（LLMs）中显示的幂律缩放现象。具体来说，随着真实域的数量或深度伪造方法的增加，平均检测误差遵循可预测的幂律衰减。数据集结构包括训练集和验证集，分别包含真实和伪造的人脸图像。此外，数据集还提供了其他已建立的基准测试的适配版本。

ScaleDF is the largest dataset in the deepfake detection domain to date, containing over 5.8 million real images from 51 different datasets (domains) and more than 8.8 million fake images generated by 102 deepfake methods. Using ScaleDF, researchers can observe power-law scaling similar to that shown in large language models (LLMs). Specifically, the average detection error follows a predictable power-law decay as either the number of real domains or the number of deepfake methods increases. The dataset structure includes training and validation sets, each containing real and fake face images. Additionally, the dataset provides adapted versions of other established benchmarks.