Protecting World Leaders Against Deep Fakes

175 indexed citations

Abstract

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This paper, published in 2019, received 175 indexed citations. Written by Shruti Agarwal, Hany Farid, Yuming Gu, Mingming He, Koki Nagano and Hao Li covering the research area of Information Systems, Sociology and Political Science and Signal Processing. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (149 citations), Artificial Intelligence (41 citations) and Sociology and Political Science (22 citations). Published in Computer Vision and Pattern Recognition.

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Countries where authors are citing Protecting World Leaders Against Deep Fakes

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Citations

This map shows the geographic impact of Protecting World Leaders Against Deep Fakes. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Protecting World Leaders Against Deep Fakes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Protecting World Leaders Against Deep Fakes more than expected).

Fields of papers citing Protecting World Leaders Against Deep Fakes

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Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of Protecting World Leaders Against Deep Fakes. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Protecting World Leaders Against Deep Fakes.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

This paper is also available at doi.org/w8190960.

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