  The rapid advancement of deep learning models that can generate and synthesis
hyper-realistic videos known as Deepfakes and their ease of access to the
general public have raised concern from all concerned bodies to their possible
malicious intent use. Deep learning techniques can now generate faces, swap
faces between two subjects in a video, alter facial expressions, change gender,
and alter facial features, to list a few. These powerful video manipulation
methods have potential use in many fields. However, they also pose a looming
threat to everyone if used for harmful purposes such as identity theft,
phishing, and scam. In this work, we propose a Convolutional Vision Transformer
for the detection of Deepfakes. The Convolutional Vision Transformer has two
components: Convolutional Neural Network (CNN) and Vision Transformer (ViT).
The CNN extracts learnable features while the ViT takes in the learned features
as input and categorizes them using an attention mechanism. We trained our
model on the DeepFake Detection Challenge Dataset (DFDC) and have achieved 91.5
percent accuracy, an AUC value of 0.91, and a loss value of 0.32. Our
contribution is that we have added a CNN module to the ViT architecture and
have achieved a competitive result on the DFDC dataset.
