  Deepfakes have raised significant concerns due to their potential to spread
false information and compromise digital media integrity. In this work, we
propose a Generative Convolutional Vision Transformer (GenConViT) for deepfake
video detection. Our model combines ConvNeXt and Swin Transformer models for
feature extraction, and it utilizes Autoencoder and Variational Autoencoder to
learn from the latent data distribution. By learning from the visual artifacts
and latent data distribution, GenConViT achieves improved performance in
detecting a wide range of deepfake videos. The model is trained and evaluated
on DFDC, FF++, DeepfakeTIMIT, and Celeb-DF v2 datasets, achieving high
classification accuracy, F1 scores, and AUC values. The proposed GenConViT
model demonstrates robust performance in deepfake video detection, with an
average accuracy of 95.8% and an AUC value of 99.3% across the tested datasets.
Our proposed model addresses the challenge of generalizability in deepfake
detection by leveraging visual and latent features and providing an effective
solution for identifying a wide range of fake videos while preserving media
integrity. The code for GenConViT is available at
https://github.com/erprogs/GenConViT.
