  Deepfakes are the synthesized digital media in order to create
ultra-realistic fake videos to trick the spectator. Deep generative algorithms,
such as, Generative Adversarial Networks(GAN) are widely used to accomplish
such tasks. This approach synthesizes pseudo-realistic contents that are very
difficult to distinguish by traditional detection methods. In most cases,
Convolutional Neural Network(CNN) based discriminators are being used for
detecting such synthesized media. However, it emphasise primarily on the
spatial attributes of individual video frames, thereby fail to learn the
temporal information from their inter-frame relations. In this paper, we
leveraged an optical flow based feature extraction approach to extract the
temporal features, which are then fed to a hybrid model for classification.
This hybrid model is based on the combination of CNN and recurrent neural
network (RNN) architectures. The hybrid model provides effective performance on
open source data-sets such as, DFDC, FF++ and Celeb-DF. This proposed method
shows an accuracy of 66.26%, 91.21% and 79.49% in DFDC, FF++, and Celeb-DF
respectively with a very reduced No of sample size of approx 100
samples(frames). This promises early detection of fake contents compared to
existing modalities.
