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...

Full description

Saved in:
Bibliographic Details
Published in:Mathematics (Basel) 2023-09, Vol.11 (17), p.3710
Main Authors: Arshed, Muhammad Asad, Alwadain, Ayed, Faizan Ali, Rao, Mumtaz, Shahzad, Ibrahim, Muhammad, Muneer, Amgad
Format: Article
Language:English
Subjects:
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
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11173710