A review of deep learning‐based approaches for deepfake content detection

Author:

Passos Leandro A.1ORCID,Jodas Danilo1ORCID,Costa Kelton A. P.1ORCID,Souza Júnior Luis A.1ORCID,Rodrigues Douglas1ORCID,Del Ser Javier23ORCID,Camacho David4ORCID,Papa João Paulo1ORCID

Affiliation:

1. Department of Computing São Paulo State University Av. Eng. Luiz Edmundo Carrijo Coube Bauru Brazil

2. TECNALIA, Basque Research and Technology Alliance (BRTA) Derio Spain

3. Department of Communications Engineering University of the Basque Country (UPV/EHU) Bilbao Spain

4. School of Computer Systems Engineering Universidad Politécnica de Madrid Madrid Spain

Abstract

AbstractRecent 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.

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Fundação de Amparo à Pesquisa do Estado de São Paulo

Eusko Jaurlaritza

Publisher

Wiley

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