A Decade of Deepfake Research in the Generative AI Era, 2014–2024: A Bibliometric Analysis

Abstract

The recent growth of generative artificial intelligence (AI) has brought new possibilities and revolutionary applications in many fields. It has also, however, created important ethical and security issues, especially with the abusive use of deepfakes, which are artificial media that can propagate very realistic but false information. This paper provides an extensive bibliometric, statistical, and trend analysis of deepfake research in the age of generative AI. Utilizing the Web of Science (WoS) database for the years 2014–2024, the research identifies key authors, influential publications, collaboration networks, and leading institutions. Biblioshiny (Bibliometrix R package, University of Naples Federico II, Naples, Italy) and VOSviewer (version 1.6.20, Centre for Science and Technology Studies, Leiden University, Leiden, The Netherlands) are utilized in the research for mapping the science production, theme development, and geographical distribution. The cutoff point of ten keyword frequencies by occurrence was applied to the data for relevance. This study aims to provide a comprehensive snapshot of the research status, identify gaps in the knowledge, and direct upcoming studies in the creation, detection, and mitigation of deepfakes. The study is intended to help researchers, developers, and policymakers understand the trajectory and impact of deepfake technology, supporting innovation and governance strategies. The findings highlight a strong average annual growth rate of 61.94% in publications between 2014 and 2024, with China, the United States, and India as leading contributors, IEEE Access among the most influential sources, and three dominant clusters emerging around disinformation, generative models, and detection methods.