A Bibliometric Analysis of Deepfakes : Trends, Applications and Challenges

Diya Garg, Rupali Gill
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Abstract

INTRODUCTION: The rapid progress in artificial intelligence (AI) over the past decade has ushered in a new era of transformative technologies. Deep learning has emerged as a potential tool, demonstrating remarkable capabilities in various applications. This paper focuses on one of the controversial applications of deep learning commonly known as deepfakes.OBJECTIVES: The main objective of this comprehensive bibliometric survey is to explore the trends, applications and challenges of deepfakes over the course of last 4.5 years.METHODS: In this research, a total of 794 documents published from 2019 to July 2023 were acquired from Scopus database. To conduct this bibliometric analysis, RStudio and VOSviewer tools have been used. In this current analysis, deepfake challenges, countries, sources, top 20 cited documents, and research trends in the field of deepfake have been included.RESULTS: The analysis highlights a substantial increase in deepfake publications from January 2019 to July 2023. Out of the 8 document types identified 38% are article publications. In addition, from the journal articles it has been depicted that the journal source entitled "Advances in Computer Vision and Pattern Recognition" holds Q1 status with 8.3% publications in the deepfakes domain during the targeted year range. Moreover, the data visualizations reveal the growing international collaboration, with the USA as the most prolific country in deepfake research.CONCLUSION: Despite numerous reviews on deepfakes, there has been a notable absence of comprehensive scientometric analyses. This paper fills this gap through a bibliometric study using the Scopus database as urderlying source. The analysis includes keyword analysis, leading research-contributing institutes, co-country collaboration, and co-keyword occurrence. The findings offer valuable insights for scholars, providing a foundational understanding including document types, prominent journals, international collaboration trends, and influential institutions and offering valuable guidance for future scholarly pursuits in this evolving field.
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深度伪造的文献计量分析:趋势、应用与挑战
导言:过去十年,人工智能(AI)取得了突飞猛进的发展,开创了变革性技术的新时代。深度学习已成为一种潜在的工具,在各种应用中展现出非凡的能力。本文将重点讨论深度学习的一种有争议的应用,即通常所说的深度伪造:方法:本研究从 Scopus 数据库中获取了 2019 年至 2023 年 7 月间发表的 794 篇文献。为了进行文献计量分析,使用了 RStudio 和 VOSviewer 工具。结果:分析结果表明,从2019年1月到2023年7月,deepfake出版物大幅增加。在确定的 8 种文献类型中,38% 是文章出版物。此外,从期刊论文来看,题为《计算机视觉与模式识别进展》(Advances in Computer Vision and Pattern Recognition)的期刊源在目标年份范围内以 8.3% 的深度伪造领域出版物占据第一位。此外,数据可视化显示国际合作日益增多,美国是深度伪造研究最活跃的国家。本文以 Scopus 数据库为基础,通过文献计量学研究填补了这一空白。分析包括关键词分析、主要研究贡献机构、合作国家和共同关键词出现情况。研究结果为学者们提供了宝贵的见解,使他们对文献类型、著名期刊、国际合作趋势和有影响力的机构有了基本的了解,并为今后在这一不断发展的领域开展学术研究提供了宝贵的指导。
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