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Vit Deepfake Detection
V
Vit Deepfake Detection
Developed by Wvolf
This model was trained by Rudolf Enyimba for detecting deepfake images, achieving a test accuracy of 98.70%.
Downloads 1,990
Release Time : 1/4/2024
Model Overview
A deep learning model specifically designed for detecting deepfake images, suitable for authenticity verification of facial images.
Model Features
High accuracy
Achieves an outstanding accuracy of 98.70% on the test set.
Deepfake detection
Specifically optimized for deepfake images, effectively identifying AI-processed facial images.
Model Capabilities
Image classification
Deepfake detection
Facial authenticity verification
Use Cases
Security verification
Social media content moderation
Detect deepfake facial images circulating on social media
Can effectively identify 98.7% of forged content
Identity verification systems
Enhance the security of biometric systems to prevent fake facial attacks
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