  In recent years, deep learning-based video manipulation methods have become
widely accessible to masses. With little to no effort, people can easily learn
how to generate deepfake videos with only a few victims or target images. This
creates a significant social problem for everyone whose photos are publicly
available on the Internet, especially on social media websites. Several deep
learning-based detection methods have been developed to identify these
deepfakes. However, these methods lack generalizability, because they perform
well only for a specific type of deepfake method. Therefore, those methods are
not transferable to detect other deepfake methods. Also, they do not take
advantage of the temporal information of the video. In this paper, we addressed
these limitations. We developed a Convolutional LSTM based Residual Network
(CLRNet), which takes a sequence of consecutive images as an input from a video
to learn the temporal information that helps in detecting unnatural looking
artifacts that are present between frames of deepfake videos. We also propose a
transfer learning-based approach to generalize different deepfake methods.
Through rigorous experimentations using the FaceForensics++ dataset, we showed
that our method outperforms five of the previously proposed state-of-the-art
deepfake detection methods by better generalizing at detecting different
deepfake methods using the same model.
