A review of deep learning‐based approaches for deepfake content detection
Leandro A. Passos, Danilo Samuel Jodas, Kelton Augusto Pontara da Costa, Luis A. de Souza, Douglas Rodrigues, Javier Del Ser, David Camacho, João Paulo Papa
Abstract
Abstract 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.