Manipulated videos, especially those where the identity of an individual has been modified using deep neural networks, are becoming an increasingly relevant threat in the modern day. In this paper, we seek to develop a generalizable, explainable solution to detecting these manipulated videos. To achieve this, we design a series of forgery detection systems that each focus on one individual part of the face. These parts-based detection systems, which can be combined and used together in a single architecture, meet all of our desired criteria - they generalize effectively between datasets and give us valuable insights into what the network is looking at when making its decision. We thus use these detectors to perform detailed empirical analysis on the FaceForensics++, Celeb-DF, and Facebook Deepfake Detection Challenge datasets, examining not just what the detectors find but also collecting and analyzing useful related statistics on the datasets themselves.

翻译：操纵视频,特别是那些使用深层神经网络对个人身份进行修改的视频,在现代日益成为具有现实意义的威胁。在本文中,我们寻求为检测这些被操纵的视频开发一个普遍、可解释的解决方案。为了实现这一目标,我们设计了一系列伪造检测系统,每个系统都关注面部的某一个部分。这些基于部件的检测系统可以在一个单一的结构中结合使用,它们符合我们的所有期望标准 — — 它们有效地在数据集之间进行概括,并给我们提供有价值的洞察,说明网络在作出决定时所看到的情况。因此,我们利用这些探测器对FaceForensics++、Celeb-DF和Facebook Deepfake探测器挑战数据集进行详细的实证分析,不仅检查探测器发现的内容,而且还收集和分析有关数据集本身的有用相关统计数据。