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A review on deepfake generation and detection: bibliometric analysis

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Abstract

Deepfake refers to an artificial intelligence-based technique to produce manipulated videos that look realistic. However, this good aspect of Deepfake sometimes pose serious threats to society as malicious intenet users exploit deepfakes to disseminate false information. That’s why a lot of research and applications are being developed in both the deepfake generation as well as detection field to mitigate its negative effect. Given the intensity of the work and its imporatnce in the field, there is a need to comprehensively review the existing literature and provide probable directions for future works considering the identified gaps and limitations. So, in this paper, a bibliometric analysis is conducted to provide a comprehensive analysis of this topic from various aspects such as influential authors who are active in this field and their collaboration, as well as countries and more specific institutions investing annually. In this study, the primary source of data is Web of Science (WoS), and we’ve employed keyword-based searches to retrieve pertinent information. The collected data from WoS has been scrutinized along several dimensions, including top document types, publication trends, source titles, and the productivity of various locations with an annual breakdown. To delve into collaborative efforts among institutions, authors, and regions, we utilized the VOSviewer tool. Additionally, the CiteSpace tool aided in identifying key focal points, research trajectories, and pinpointing significant shifts in citations for keywords. This comprehensive approach contributes to the in-depth analysis presented in this paper.

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Data and code Availability

The data and code used for the experiments in this research project is available upon request via mail.

Notes

  1. https://www.reddit.com/r/DeepFakeOne

  2. https://github.com/Deepfakes/

  3. https://www.kaggle.com/c/deepfake-detection-challenge

  4. https://github.com/ondyari/FaceForensics/tree/master/dataset

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AK conceived the idea, collected the data, perform the analysis and wrote the original manuscript. SK performed writing - review and editing. RK identified the problem and provided guidance throughout the project.

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Correspondence to Rajeev Kumar.

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Kaushal, A., Kumar, S. & Kumar, R. A review on deepfake generation and detection: bibliometric analysis. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-18706-x

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  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11042-024-18706-x

Keywords

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