Comparative study of deep learning techniques for DeepFake video detection
R. A. Khan, Muhammad Sohail, Imran Usman, Muhammad Moid Sandhu, Mohsin Raza, Muhammad Azfar Yaqub, Antonio Liotta
Abstract
Deep learning addresses a wide range of complex challenges, spanning from computer vision to data analytics. It is also employed to develop softwares that pose threats to privacy and security. To develop a DeepFake video, an individual in the original video is replaced with someone else using deep learning. Various deep learning-based techniques have been proposed to detect DeepFakes. In this work, we extensively analyse DeepFake video detection techniques considering their strengths and limitations. We provide a comparative analysis along with discussing their architectures and performances. Finally, we propose hyperparameter settings that improve deep learning model’s overall accuracy and efficiency.