论文标题
基于CNN的DeepFake视频检测中的培训策略和数据增强
Training Strategies and Data Augmentations in CNN-based DeepFake Video Detection
论文作者
论文摘要
Deepfake视频的数量和质量的快速增长呼吁开发可靠的检测系统，能够在社交媒体和Internet上自动警告用户此类内容的潜在不实现。尽管算法，软件和智能手机应用程序每天都在生成操纵视频和交换面上都在变得更好，但视频中的自动化系统的精确度仍然非常有限，并且通常偏向于设计和训练特定检测系统的数据集。在本文中，我们分析了不同的培训策略和数据增强技术如何影响基于CNN的DeepFake探测器在同一数据集或不同数据集上的训练和测试时。
The fast and continuous growth in number and quality of deepfake videos calls for the development of reliable detection systems capable of automatically warning users on social media and on the Internet about the potential untruthfulness of such contents. While algorithms, software, and smartphone apps are getting better every day in generating manipulated videos and swapping faces, the accuracy of automated systems for face forgery detection in videos is still quite limited and generally biased toward the dataset used to design and train a specific detection system. In this paper we analyze how different training strategies and data augmentation techniques affect CNN-based deepfake detectors when training and testing on the same dataset or across different datasets.
