| | --- |
| | license: apache-2.0 |
| | metrics: |
| | - accuracy |
| | base_model: |
| | - google/vit-base-patch16-224-in21k |
| | pipeline_tag: image-classification |
| | language: |
| | - en |
| | library_name: transformers |
| | tags: |
| | - Python |
| | - Deepfake |
| | --- |
| | # Deepfake Image Detection Using Fine-Tuned Vision Transformer (ViT) |
| | This project focuses on detecting **deepfake images** using a fine-tuned version of the pre-trained model `google/vit-base-patch16-224-in21k`. The approach leverages the power of Vision Transformers (ViT) to classify images as real or fake. |
| |
|
| | ## **Model Overview** |
| | - **Base Model**: [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) |
| | - **Dataset**: [DFDC](https://ai.meta.com/datasets/dfdc). |
| | - **Classes**: Deepfake and Real |
| | - **Performance**: |
| | - **Validation Accuracy**: 95% |
| | - **Test Accuracy**: 91% |
| |
|
| | *Figure : Confusion matrix for test data* |
| |
|
| |  |
