Deepfake Video Detection with Vision Transformer (ViT)

This project detects deepfake videos using a Vision Transformer (ViT) model, classifying frames as real or manipulated with high accuracy.

Table of Contents

• Dataset Preparation
• Model Architecture
• Training Process
• Validation and Metrics
• Video Prediction
• Installation and Setup
• Results
• Website Usage

Dataset Preparation

Video Directories:

• Real Videos: /DFD_original_sequences
• Manipulated Videos: /DFD_manipulated_sequences

Frame Extraction:

Extract frames at 1 frame per second for model input.

Model Architecture

• Base Model: ViT (vit_base_patch16_224)
• Input Size: 224x224 pixels
• Classes: 2 (Real, Manipulated)
• Pretrained Weights: Yes (ImageNet)

Website Usage