Work

Comparative study of deep learning techniques for DeepFake video detection

Year: 2024

Type: article

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.

Source: ICT Express

DOI: 10.1016/j.icte.2024.09.018