  In this paper, we propose to detect forged videos, of faces, in online
videos. To facilitate this detection, we propose to use smaller (fewer
parameters to learn) convolutional neural networks (CNN), for a data-driven
approach to forged video detection. To validate our approach, we investigate
the FaceForensics public dataset detailing both frame-based and video-based
results. The proposed method is shown to outperform current state of the art.
We also perform an ablation study, analyzing the impact of batch size, number
of filters, and number of network layers on the accuracy of detecting forged
videos.
