Deepfake Detection Using ViT

🚀 Deepfake Image Detection Using Fine-Tuned Vision Transformer (ViT)

This project utilizes a fine - tuned pre - trained model google/vit - base - patch16 - 224 - in21k to detect deepfake images. It harnesses the capabilities of Vision Transformers (ViT) to classify images as either real or fake.

🚀 Quick Start

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.

✨ Features

• Base Model: [google/vit - base - patch16 - 224 - in21k](https://huggingface.co/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


Figure 2: Confusion matrix for validation data


💻 Usage Examples

Basic Usage

from transformers import AutoImageProcessor, AutoModelForImageClassification
import torch
from PIL import Image

# Load the image_processor and model
image_processor = AutoImageProcessor.from_pretrained('ashish-001/deepfake-detection-using-ViT')
model = AutoModelForImageClassification.from_pretrained('ashish-001/deepfake-detection-using-ViT')
# Example usage
image = Image.open('path of the image')
inputs = image_processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
pred = torch.argmax(logits, dim=1).item()
label = 'Real' if pred == 1 else 'Fake'
print(f"Predicted type: {label}")

📄 License

This project is licensed under the Apache - 2.0 license.

📚 Documentation