Year: 2024
Type: article
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.
Source: Expert Systems
DOI: 10.1111/exsy.13570
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1173679995, G1612158077, G1842211542, G2486662831, G295657101, G3041655236, G317820366, G3572101131, G3603157925, G3680712625, G3797093575, G4728310430, Center for Artificial Intelligence, G5282948760, G6438247056, On the application of Explainable Artificial Intelligence (XAI) techniques for generating images from data packages to detect anomalies in computer networks, G6669552033, G7038554653, G713732004, G757060445, G771454403, G8135300706, G815272514, G831127782
OpenAlex ID: W4392774535