Replication Data for: 'Do Deepfakes Adequately Display Emotions? A Study on Deepfake Facial Emotion Expression'
Recent technological advancements in Artificial Intelligence make it easy to create deepfakes, hyper-realistic videos in which images and video clips are processed to create fake videos that appear authentic. Many of them are based on swapping faces without the consent of the person whose appearance...
| Autores: | , , |
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| Formato: | conjunto de datos |
| Fecha de publicación: | 2022 |
| País: | España |
| Recursos: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:10459.1/83929 |
| Acesso em linha: | https://doi.org/10.34810/data262 |
| Access Level: | acceso abierto |
| Palavra-chave: | Computer and Information Science Affective Computing Deepfake Emotion Recognition |
| Resumo: | Recent technological advancements in Artificial Intelligence make it easy to create deepfakes, hyper-realistic videos in which images and video clips are processed to create fake videos that appear authentic. Many of them are based on swapping faces without the consent of the person whose appearance and voice are used. As emotions are inherent in human communication, studying how deepfakes transfer emotional expressions from original to fakes is relevant. In this work, we conduct an in-depth study on facial emotional expression in deepfakes using a well-known face swap-based deepfake database. First, we extracted the photograms from their videos. Then, we analyzed the emotional expression in both the original and the faked versions of the video recordings for all performers in the database. Results show that emotional expressions are not adequately transferred between original recordings and the deepfakes created from them. The high variability in emotions and performers detected between original and fake recordings indicates that performer emotion expressiveness should be considered for better deepfake generation or for detecting them. |
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