  With the arrival of several face-swapping applications such as FaceApp,
SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital
media content is hanging on a very loose thread. On social media platforms,
videos are widely circulated often at a high compression factor. In this work,
we analyze several deep learning approaches in the context of deepfakes
classification in high compression scenario and demonstrate that a proposed
approach based on metric learning can be very effective in performing such a
classification. Using less number of frames per video to assess its realism,
the metric learning approach using a triplet network architecture proves to be
fruitful. It learns to enhance the feature space distance between the cluster
of real and fake videos embedding vectors. We validated our approaches on two
datasets to analyze the behavior in different environments. We achieved a
state-of-the-art AUC score of 99.2% on the Celeb-DF dataset and accuracy of
90.71% on a highly compressed Neural Texture dataset. Our approach is
especially helpful on social media platforms where data compression is
inevitable.
