  This paper proposes a new DeepFake detector FakeBuster for detecting
impostors during video conferencing and manipulated faces on social media.
FakeBuster is a standalone deep learning based solution, which enables a user
to detect if another person's video is manipulated or spoofed during a video
conferencing based meeting. This tool is independent of video conferencing
solutions and has been tested with Zoom and Skype applications. It uses a 3D
convolutional neural network for predicting video segment-wise fakeness scores.
The network is trained on a combination of datasets such as Deeperforensics,
DFDC, VoxCeleb, and deepfake videos created using locally captured (for video
conferencing scenarios) images. This leads to different environments and
perturbations in the dataset, which improves the generalization of the deepfake
network.
