RT Journal Article
T1 A review of deep learning-based approaches for deepfake content detection
A1 Passos, Leandro A.
A1 Jodas, Danilo
A1 Costa, Kelton A.P.
A1 Souza Júnior, Luis A.
A1 Rodrigues, Douglas
A1 Del Ser, Javier
A1 Camacho, David
A1 Papa, João Paulo
AB Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works, and prescribe several future directions towards the issues and shortcomings still unsolved on deepfake detection.
SN 0266-4720
YR 2024
FD 2024
LK https://hdl.handle.net/11556/4973
UL https://hdl.handle.net/11556/4973
LA eng
NO Passos , L A , Jodas , D , Costa , K A P , Souza Júnior , L A , Rodrigues , D , Del Ser , J , Camacho , D & Papa , J P 2024 , ' A review of deep learning-based approaches for deepfake content detection ' , Expert Systems . https://doi.org/10.1111/exsy.13570
NO Publisher Copyright: © 2024 John Wiley & Sons Ltd.
DS TECNALIA Publications
RD 29 sept 2024