  Conspicuous progression in the field of machine learning and deep learning
have led the jump of highly realistic fake media, these media oftentimes
referred as deepfakes. Deepfakes are fabricated media which are generated by
sophisticated AI that are at times very difficult to set apart from the real
media. So far, this media can be uploaded to the various social media
platforms, hence advertising it to the world got easy, calling for an
efficacious countermeasure. Thus, one of the optimistic counter steps against
deepfake would be deepfake detection. To undertake this threat, researchers in
the past have created models to detect deepfakes based on ML/DL techniques like
Convolutional Neural Networks. This paper aims to explore different
methodologies with an intention to achieve a cost-effective model with a higher
accuracy with different types of the datasets, which is to address the
generalizability of the dataset.
