Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions
Recent years have seen a substantial increase in interest in deepfakes, a fast-developing field at the nexus of artificial intelligence and multimedia. These artificial media creations, made possible by deep learning algorithms, allow for the manipulation and creation of digital content that is extr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Computers |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-431X/12/10/216 |
| _version_ | 1827721286874300416 |
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| author | Amal Naitali Mohammed Ridouani Fatima Salahdine Naima Kaabouch |
| author_facet | Amal Naitali Mohammed Ridouani Fatima Salahdine Naima Kaabouch |
| author_sort | Amal Naitali |
| collection | DOAJ |
| description | Recent years have seen a substantial increase in interest in deepfakes, a fast-developing field at the nexus of artificial intelligence and multimedia. These artificial media creations, made possible by deep learning algorithms, allow for the manipulation and creation of digital content that is extremely realistic and challenging to identify from authentic content. Deepfakes can be used for entertainment, education, and research; however, they pose a range of significant problems across various domains, such as misinformation, political manipulation, propaganda, reputational damage, and fraud. This survey paper provides a general understanding of deepfakes and their creation; it also presents an overview of state-of-the-art detection techniques, existing datasets curated for deepfake research, as well as associated challenges and future research trends. By synthesizing existing knowledge and research, this survey aims to facilitate further advancements in deepfake detection and mitigation strategies, ultimately fostering a safer and more trustworthy digital environment. |
| first_indexed | 2024-03-10T21:19:21Z |
| format | Article |
| id | doaj.art-2a1a3f65b495407fb907fd548c38b578 |
| institution | Directory Open Access Journal |
| issn | 2073-431X |
| language | English |
| last_indexed | 2024-03-10T21:19:21Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Computers |
| spelling | doaj.art-2a1a3f65b495407fb907fd548c38b5782023-11-19T16:08:14ZengMDPI AGComputers2073-431X2023-10-01121021610.3390/computers12100216Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research DirectionsAmal Naitali0Mohammed Ridouani1Fatima Salahdine2Naima Kaabouch3RITM Laboratory, CED Engineering Sciences, Hassan II University, Casablanca 20000, MoroccoRITM Laboratory, CED Engineering Sciences, Hassan II University, Casablanca 20000, MoroccoDepartment of Electrical and Computer Engineering, The University of North Carolina at Charlotte, Charlotte, NC 28223, USASchool of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND 58202, USARecent years have seen a substantial increase in interest in deepfakes, a fast-developing field at the nexus of artificial intelligence and multimedia. These artificial media creations, made possible by deep learning algorithms, allow for the manipulation and creation of digital content that is extremely realistic and challenging to identify from authentic content. Deepfakes can be used for entertainment, education, and research; however, they pose a range of significant problems across various domains, such as misinformation, political manipulation, propaganda, reputational damage, and fraud. This survey paper provides a general understanding of deepfakes and their creation; it also presents an overview of state-of-the-art detection techniques, existing datasets curated for deepfake research, as well as associated challenges and future research trends. By synthesizing existing knowledge and research, this survey aims to facilitate further advancements in deepfake detection and mitigation strategies, ultimately fostering a safer and more trustworthy digital environment.https://www.mdpi.com/2073-431X/12/10/216deepfake detectionface forgerydeep learninggenerative artificial intelligencevision transformers |
| spellingShingle | Amal Naitali Mohammed Ridouani Fatima Salahdine Naima Kaabouch Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions Computers deepfake detection face forgery deep learning generative artificial intelligence vision transformers |
| title | Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions |
| title_full | Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions |
| title_fullStr | Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions |
| title_full_unstemmed | Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions |
| title_short | Deepfake Attacks: Generation, Detection, Datasets, Challenges, and Research Directions |
| title_sort | deepfake attacks generation detection datasets challenges and research directions |
| topic | deepfake detection face forgery deep learning generative artificial intelligence vision transformers |
| url | https://www.mdpi.com/2073-431X/12/10/216 |
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