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 · ICT Express · 2024
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.