Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network
With the development of image-generating technologies, significant progress has been made in the field of facial manipulation techniques. These techniques allow people to easily modify media information, such as videos and images, by substituting the identity or facial expression of one person with...
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| Published in | Mathematics (Basel) Vol. 11; no. 17; p. 3710 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Basel
MDPI AG
01.09.2023
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| Abstract | With the development of image-generating technologies, significant progress has been made in the field of facial manipulation techniques. These techniques allow people to easily modify media information, such as videos and images, by substituting the identity or facial expression of one person with the face of another. This has significantly increased the availability and accessibility of such tools and manipulated content termed ‘deepfakes’. Developing an accurate method for detecting fake images needs time to prevent their misuse and manipulation. This paper examines the capabilities of the Vision Transformer (ViT), i.e., extracting global features to detect deepfake images effectively. After conducting comprehensive experiments, our method demonstrates a high level of effectiveness, achieving a detection accuracy, precision, recall, and F1 rate of 99.5 to 100% for both the original and mixture data set. According to our existing understanding, this study is a research endeavor incorporating real-world applications, specifically examining Snapchat-filtered images. |
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| AbstractList | With the development of image-generating technologies, significant progress has been made in the field of facial manipulation techniques. These techniques allow people to easily modify media information, such as videos and images, by substituting the identity or facial expression of one person with the face of another. This has significantly increased the availability and accessibility of such tools and manipulated content termed ‘deepfakes’. Developing an accurate method for detecting fake images needs time to prevent their misuse and manipulation. This paper examines the capabilities of the Vision Transformer (ViT), i.e., extracting global features to detect deepfake images effectively. After conducting comprehensive experiments, our method demonstrates a high level of effectiveness, achieving a detection accuracy, precision, recall, and F1 rate of 99.5 to 100% for both the original and mixture data set. According to our existing understanding, this study is a research endeavor incorporating real-world applications, specifically examining Snapchat-filtered images. |
| Audience | Academic |
| Author | Arshed, Muhammad Asad Alwadain, Ayed Faizan Ali, Rao Ibrahim, Muhammad Muneer, Amgad Mumtaz, Shahzad |
| Author_xml | – sequence: 1 givenname: Muhammad Asad surname: Arshed fullname: Arshed, Muhammad Asad – sequence: 2 givenname: Ayed surname: Alwadain fullname: Alwadain, Ayed – sequence: 3 givenname: Rao orcidid: 0000-0003-0701-6761 surname: Faizan Ali fullname: Faizan Ali, Rao – sequence: 4 givenname: Shahzad orcidid: 0000-0003-2606-2405 surname: Mumtaz fullname: Mumtaz, Shahzad – sequence: 5 givenname: Muhammad orcidid: 0000-0001-5088-9571 surname: Ibrahim fullname: Ibrahim, Muhammad – sequence: 6 givenname: Amgad orcidid: 0000-0002-7157-3020 surname: Muneer fullname: Muneer, Amgad |
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| SubjectTerms | Accuracy Artificial intelligence Deception Deep learning deepfake fine tuning Forgery Identification Image filters Image manipulation Literature reviews Machine learning Mathematics Methods pretrained Social networks Vision Transformer |
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| Title | Unmasking Deception: Empowering Deepfake Detection with Vision Transformer Network |
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