Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects
In recent years, rapid advancements in deepfakes (incorporating Artificial Intelligence (AI), machine, and deep learning) have updated tools and techniques for manipulating multimedia. Though technology has primarily been utilized for beneficial purposes, such as education and entertainment, it is a...
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2025-01-01
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Online Access: | https://ieeexplore.ieee.org/document/10816641/ |
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author | Naveed Ur Rehman Ahmed Afzal Badshah Hanan Adeel Ayesha Tajammul Ali Daud Tariq Alsahfi |
author_facet | Naveed Ur Rehman Ahmed Afzal Badshah Hanan Adeel Ayesha Tajammul Ali Daud Tariq Alsahfi |
author_sort | Naveed Ur Rehman Ahmed |
collection | DOAJ |
description | In recent years, rapid advancements in deepfakes (incorporating Artificial Intelligence (AI), machine, and deep learning) have updated tools and techniques for manipulating multimedia. Though technology has primarily been utilized for beneficial purposes, such as education and entertainment, it is also used for malicious or unethical tasks to spread disinformation or ruin someone’s dignity, even if it encompasses harassing and blackmailing victims. Deepfakes refer to high-quality and realistic multimedia-manipulated content that has been digitally modified or synthetically generated. We conducted a systematic literature review of deepfake detection to offer an updated overview of existing research work that initially describes the widely accessible deepfake generation tools, classifications, and detection process. We highlighted recent techniques in visual deepfake detection based on the feature representations, grouped into four domains: spatial, temporal, frequency, and spatio-temporal, including their key features and limitations by providing details of existing datasets, together with the potentials of deepfake and its future directions. This study tried to add an updated repository of technological change in deepfake, which could help researchers to develop robust deepfake models. |
format | Article |
id | doaj-art-c4869578eeb0480ca984a92b12fd7523 |
institution | Kabale University |
issn | 2169-3536 |
language | English |
publishDate | 2025-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj-art-c4869578eeb0480ca984a92b12fd75232025-01-07T00:01:39ZengIEEEIEEE Access2169-35362025-01-01131923196110.1109/ACCESS.2024.352328810816641Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future ProspectsNaveed Ur Rehman Ahmed0Afzal Badshah1https://orcid.org/0000-0002-3444-4609Hanan Adeel2Ayesha Tajammul3Ali Daud4https://orcid.org/0000-0002-8284-6354Tariq Alsahfi5https://orcid.org/0000-0003-4299-1626Department of Computing, Hamdard University, Islamabad Campus, Islamabad, PakistanDepartment of Software Engineering, University of Sargodha, Sargodha, PakistanDepartment of Computing, Hamdard University, Islamabad Campus, Islamabad, PakistanU.S.-Pakistan Center for Advanced Studies in Water, Mehran University of Engineering and Technology, Jamshoro, Sindh, PakistanFaculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab EmiratesDepartment of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaIn recent years, rapid advancements in deepfakes (incorporating Artificial Intelligence (AI), machine, and deep learning) have updated tools and techniques for manipulating multimedia. Though technology has primarily been utilized for beneficial purposes, such as education and entertainment, it is also used for malicious or unethical tasks to spread disinformation or ruin someone’s dignity, even if it encompasses harassing and blackmailing victims. Deepfakes refer to high-quality and realistic multimedia-manipulated content that has been digitally modified or synthetically generated. We conducted a systematic literature review of deepfake detection to offer an updated overview of existing research work that initially describes the widely accessible deepfake generation tools, classifications, and detection process. We highlighted recent techniques in visual deepfake detection based on the feature representations, grouped into four domains: spatial, temporal, frequency, and spatio-temporal, including their key features and limitations by providing details of existing datasets, together with the potentials of deepfake and its future directions. This study tried to add an updated repository of technological change in deepfake, which could help researchers to develop robust deepfake models.https://ieeexplore.ieee.org/document/10816641/Deepfake detectionmachine learningdeep learningdeepfake applicationsdeepfake datasetsdeepfake generation tools |
spellingShingle | Naveed Ur Rehman Ahmed Afzal Badshah Hanan Adeel Ayesha Tajammul Ali Daud Tariq Alsahfi Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects IEEE Access Deepfake detection machine learning deep learning deepfake applications deepfake datasets deepfake generation tools |
title | Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects |
title_full | Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects |
title_fullStr | Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects |
title_full_unstemmed | Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects |
title_short | Visual Deepfake Detection: Review of Techniques, Tools, Limitations, and Future Prospects |
title_sort | visual deepfake detection review of techniques tools limitations and future prospects |
topic | Deepfake detection machine learning deep learning deepfake applications deepfake datasets deepfake generation tools |
url | https://ieeexplore.ieee.org/document/10816641/ |
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