Deepfake Generation, Detection and Datasets: a Rapid-review

Abstract

Deepfake is the general expression of fake images or videos. Images and videos created with Deep Neural Networks (DNN) can cause both personal and national problems. Deepfake technology has been making progress day by day. Accordingly, both the detection methods and the diversity of the data sets to be trained are increasing. In this article, datasets created in deepfake technology and applications that use datasets for deepfake detection are evaluated. The data sets used are produced with different models. There exist both traditional and deep learning-based detection methods for deepfake detection. Due to the recent malicious use of deepfake technology, detection methods and methods to be used in applications are of great importance. When the detection methods are compared, the accuracy of the deep learning-based detection method is higher for each data set. The method that provides the highest accuracy among two different data sets is the Xception method.