Deepfake video detection using deep learning algorithms

Deepfake videos are one of the areas that have attracted a lot of attention, especially in recent years. As a result of the increasing popularity of social networks, editing and sharing of videos and images created with advanced cameras of mobile devices has reached a more accessible level than before. Many fake images and videos created with the deepfake techniques and distributed on social networks threaten not only the private life of individuals, but also the public order. The human face always has an important role in human interaction and biometric-based verification systems. Therefore, even minor manipulations of face frames can undermine trust in security applications and digital data In this study, a classification problem solution approach is adopted in the creation of the deepfake video detection model. Pre-trained EfficientNet model family is used as feature extractor and a classifier is trained on it to get the output of the prediction. The DFCC data set, which is one of the largest deepfake datasets and produced by deep learning-based methods, was used to train the model. Deep learning algorithms and libraries have been used and a new model has been introduced that decides whether the determined video is real or fake.