A Survey on Deepfake Video Detection
Abstract Recently, deepfake videos, generated by deep learning algorithms, have attracted widespread attention. Deepfake technology can be used to perform face manipulation with high realism. So far, there have been a large amount of deepfake videos circulating on the Internet, most of which target...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-11-01
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| Series: | IET Biometrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/bme2.12031 |
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| Summary: | Abstract Recently, deepfake videos, generated by deep learning algorithms, have attracted widespread attention. Deepfake technology can be used to perform face manipulation with high realism. So far, there have been a large amount of deepfake videos circulating on the Internet, most of which target at celebrities or politicians. These videos are often used to damage the reputation of celebrities and guide public opinion, greatly threatening social stability. Although the deepfake algorithm itself has no attributes of good or evil, this technology has been widely used for negative purposes. To prevent it from threatening human society, a series of research have been launched, including developing detection methods and building large‐scale benchmarks. This review aims to demonstrate the current research status of deepfake video detection, especially, generation process, several detection methods and existing benchmarks. It has been revealed that current detection methods are still insufficient to be applied in real scenes, and further research should pay more attention to the generalization and robustness. |
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| ISSN: | 2047-4938 2047-4946 |