Year: 2024
Type: conference-paper
Abstract: Research on DeepFake detection using deep neural networks (DNNs) has gained more attention in an effort to detect and categorize DeepFakes. In essence, DeepFakes are regenerated content made by changing particular DNN model elements. In this study, a summary of DeepFake detection methods for images and videos involving faces will be given based on their effectiveness, outcomes, methodology, and type of detection method. We will analyze and categorize the many DeepFake-generating techniques now in use into five primary classes. DeepFake datasets are frequently used to train and test DeepFake models. We will also cover the latest developments in DeepFake dataset trends that are currently accessible. We will also examine the problems in building a generalized DeepFake detection model. Lastly, the difficulties in creating and identifying DeepFakes will be covered.
Authors: Osipilli Aparna, Pakanati Rani, Tulluri Ramya, Tanneru Priyanka, Neela Sundari, P. G. K. Sirisha, Repudi Ramesh, Dama Anand
Institution: Koneru Lakshmaiah Education Foundation
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4402747532