|
|
--- |
|
|
license: cc-by-nc-4.0 |
|
|
language: |
|
|
- en |
|
|
--- |
|
|
Pretrained model for <strong>Deepfake Video Detection Using Generative Convolutional Vision Transformer (GenConViT)</strong> paper. |
|
|
|
|
|
<strong>GenConViT Model Architecture</strong> |
|
|
|
|
|
The GenConViT model consists of two independent networks and incorporates the following modules: |
|
|
|
|
|
Autoencoder (AE), |
|
|
Variational Autoencoder (VAE), and |
|
|
ConvNeXt-Swin Hybrid layer |
|
|
|
|
|
GenConViT is trained using Adam optimizer with a learning rate of 0.0001 and weight decay of 0.0001. |
|
|
|
|
|
GenConViT is trained on the DFDC, FF++, and TM datasets. |
|
|
|
|
|
GenConViT model has an average accuracy of 95.8% and an AUC value of 99.3% across the tested datasets (DFDC, FF++, and DeepfakeTIMT, Celeb-DF (v2)). |
|
|
|
|
|
code link: https://github.com/erprogs/GenConViT |
|
|
|
