  : Deep learning methodologies have been used to create applications that can
cause threats to privacy, democracy and national security and could be used to
further amplify malicious activities. One of those deep learning-powered
applications in recent times is synthesized videos of famous personalities.
According to Forbes, Generative Adversarial Networks(GANs) generated fake
videos growing exponentially every year and the organization known as Deeptrace
had estimated an increase of deepfakes by 84% from the year 2018 to 2019. They
are used to generate and modify human faces, where most of the existing fake
videos are of prurient non-consensual nature, of which its estimates to be
around 96% and some carried out impersonating personalities for cyber crime. In
this paper, available video datasets are identified and a pretrained model
BlazeFace is used to detect faces, and a ResNet and Xception ensembled
architectured neural network trained on the dataset to achieve the goal of
detection of fake faces in videos. The model is optimized over a loss value and
log loss values and evaluated over its F1 score. Over a sample of data, it is
observed that focal loss provides better accuracy, F1 score and loss as the
gamma of the focal loss becomes a hyper parameter. This provides a k-folded
accuracy of around 91% at its peak in a training cycle with the real world
accuracy subjected to change over time as the model decays.
