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
Dataset: deepfake and real images .
https://www.kaggle.com/datasets/manjilkarki/deepfake-and-real-images
Classes: Binary classification (Fake, Real)
Performance:
Validation Accuracy: 97%
Test Accuracy: 92%
Figure 1: Confusion matrix for test data
