TY  - EJOU
AU  - AbdulQudus, Akanbi Bolakale 
AU  - Amodu, Oluwatosin Ahmed 
AU  - Bukar, Umar Ali 
AU  - Mahmood, Raja Azlina Raja 
AU  - Zakaria, Anies Faziehan 
AU  - Queen, Saki-Ogah 
AU  - Hanapi, Zurina Mohd 

TI  - A Contemporary and Comprehensive Bibliometric Exposition on Deepfake Research and Trends
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 84
IS  - 1
SN  - 1546-2226

AB  - This paper provides a comprehensive bibliometric exposition on deepfake research, exploring the intersection of artificial intelligence and deepfakes as well as international collaborations, prominent researchers, organizations, institutions, publications, and key themes. We performed a search on the Web of Science (WoS) database, focusing on Artificial Intelligence and Deepfakes, and filtered the results across 21 research areas, yielding 1412 articles. Using VOSviewer visualization tool, we analyzed this WoS data through keyword co-occurrence graphs, emphasizing on four prominent research themes. Compared with existing bibliometric papers on deepfakes, this paper proceeds to identify and discuss some of the highly cited papers within these themes: deepfake detection, feature extraction, face recognition, and forensics. The discussion highlights key challenges and advancements in deepfake research. Furthermore, this paper also discusses pressing issues surrounding deepfakes such as security, regulation, and datasets. We also provide an analysis of another exhaustive search on Scopus database focusing solely on Deepfakes (while not excluding AI) revealing deep learning as the predominant keyword, underscoring AI’s central role in deepfake research. This comprehensive analysis, encompassing over 500 keywords from 8790 articles, uncovered a wide range of methods, implications, applications, concerns, requirements, challenges, models, tools, datasets, and modalities related to deepfakes. Finally, a discussion on recommendations for policymakers, researchers, and other stakeholders is also provided.
KW  - Deepfake; bibliometric; deepfake detection; deep learning; recommendations

DO  - 10.32604/cmc.2025.061427