ForgeryNet

We construct the ForgeryNet dataset, an extremely large face forgery dataset with unified annotations in image- and video-level data across four tasks: 1) Image Forgery Classification, including two-way (real / fake), three-way (real / fake with identity-replaced forgery approaches / fake with identity-remained forgery approaches), and n-way (real and 15 respective forgery approaches) classification. 2) Spatial Forgery Localization, which segments the manipulated area of fake images compared to their corresponding source real images. 3) Video Forgery Classification, which re-defines the video-level forgery classification with manipulated frames in random positions. This task is important because attackers in real world are free to manipulate any target frame. and 4) Temporal Forgery Localization, to localize the temporal segments which are manipulated. ForgeryNet is by far the largest publicly available deep face forgery dataset in terms of data-scale (2.9 million images, 221,247 video

本研究构建了ForgeryNet数据集，这是一款超大规模的人脸伪造数据集，针对图像与视频级数据提供了统一标注，涵盖四项任务：1) 图像伪造分类，包含二分类（真实/伪造）、三分类（真实/采用身份替换的伪造方法生成的伪造样本/采用保留身份的伪造方法生成的伪造样本）以及多分类（真实样本与15种对应伪造方法生成的伪造样本）分类任务；2) 空间伪造定位，即相较于对应的原始真实图像，对伪造图像中的篡改区域进行分割；3) 视频伪造分类，该任务重新定义了视频级伪造分类场景，支持在随机位置插入篡改帧的情况——这一任务具备实际应用价值，因为现实世界中的攻击者可随意篡改任意目标帧；4) 时序伪造定位，即对存在篡改的时序片段进行定位。就数据规模而言，ForgeryNet是当前公开可获取的规模最大的深度人脸伪造数据集，包含290万张图像与221247个视频