Improving Video Vision Transformer for Deepfake Video Detection Using Facial Landmark, Depthwise Separable Convolution and Self Attention

Improving Video Vision Transformer for Deepfake Video Detection Using Facial Landmark, Depthwise Separable Convolution and Self Attention

In this paper, we present our result of research in video deepfake detection. We built a deepfake detection system to detect whether a video is a deepfake or real. The deepfake detection algorithm still struggle in providing a sufficient accuracy values, especially in challenging deepfake dataset. O...

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Bibliographic Details
Main Authors: Kurniawan Nur Ramadhani, Rinaldi Munir, Nugraha Priya Utama
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Deepfake detection
facial landmark
depthwise separable convolution
convolution block attention module
video vision transformer
Online Access:https://ieeexplore.ieee.org/document/10388363/

Internet

https://ieeexplore.ieee.org/document/10388363/

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