Understanding deepfakes: a comprehensive analysis of creation, generation, and detection
| dc.contributor.author | Alanazi, Sami | |
| dc.contributor.author | Asif, Seemal | |
| dc.date.accessioned | 2023-08-07T09:30:07Z | |
| dc.date.available | 2023-08-07T09:30:07Z | |
| dc.date.issued | 2023-07-24 | |
| dc.description.abstract | This paper provides a comprehensive analysis of deepfakes, focusing on their creation, generation, and detection. Deepfakes are realistic fabricated videos, images, or audios generated using artificial intelligence algorithms. While initially seen as a source of entertainment and commercial applications, the negative social consequences of deepfakes have become apparent. They are misused for creating adult content, blackmailing individuals, and spreading misinformation, leading to a decline in trust and potential societal implications. The paper also discusses the importance of legislation in regulating the use of deepfakes and explores techniques for their detection, including machine learning and natural language processing. Understanding deepfakes is essential to address their ethical and legal implications in today's digital landscape. | en_UK |
| dc.identifier.citation | Alanazi S, Asif S. (2023) Understanding deepfakes: a comprehensive analysis of creation, generation, and detection. In: 14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023) and the Affiliated Conferences, 20-24 July 2022, San Francisco, USA. Volume 72 | en_UK |
| dc.identifier.issn | 978-1-958651-48-3 | |
| dc.identifier.uri | https://doi.org/10.54941/ahfe1003290 | |
| dc.identifier.uri | https://openaccess.cms-conferences.org/publications/book/978-1-958651-48-3 | |
| dc.identifier.uri | https://dspace.lib.cranfield.ac.uk/handle/1826/20052 | |
| dc.language.iso | en | en_UK |
| dc.publisher | AHFE International | en_UK |
| dc.rights | Attribution 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.subject | Deepfakes | en_UK |
| dc.subject | Artificial intelligence | en_UK |
| dc.subject | Autoencoders | en_UK |
| dc.subject | Deep neural networks | en_UK |
| dc.subject | Detection algorithms | en_UK |
| dc.subject | Generative adversarial networks | en_UK |
| dc.subject | Fake content | en_UK |
| dc.subject | Image manipulation | en_UK |
| dc.title | Understanding deepfakes: a comprehensive analysis of creation, generation, and detection | en_UK |
| dc.type | Conference paper | en_UK |
| dcterms.dateAccepted | 2023-06-02 |