Understanding Deepfake Research and Trends through Topic Modelling

Q3 Social Sciences

Proceedings of the Association for Information Science and Technology Pub Date : 2023-10-01 DOI:10.1002/pra2.895

Chen Chen, Dion Hoe‐Lian Goh

引用次数: 0

Abstract

ABSTRACT Deepfake research has gained traction in recent years. Surveys have been conducted to summarize work on the detection and generation of deepfakes. However, a more comprehensive and quantitative overview that encompasses both technical and non‐technical areas is lacking. We address this gap using topic modelling to discover deepfake research topics in academic publications. Our results show that while detection techniques topics dominate the research field, other areas, such as privacy and legal research, offer potential avenues for further exploration.

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通过主题建模了解Deepfake研究和趋势

近年来，深度造假研究得到了广泛关注。已经进行了调查，以总结深度伪造的检测和生成工作。然而，缺乏一个更全面和定量的概述，包括技术和非技术领域。我们使用主题建模来解决这一差距，以在学术出版物中发现深度研究主题。我们的研究结果表明，虽然检测技术主题主导了研究领域，但其他领域，如隐私和法律研究，为进一步探索提供了潜在的途径。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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