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Deepfake Detection using MesoNet with PyTorch

Overview

This project implements deepfake detection using MesoNet, a convolutional neural network (CNN) designed specifically for detecting deepfake images. The model is trained on a dataset containing both real and deepfake images and is deployed for real-world deepfake image detection.

Features

• Utilizes MesoNet architecture for deepfake image detection.
• Preprocessing techniques for image dataset preparation.
• Training and evaluation procedures for model development.
• Deployment for real-world deepfake image detection.
• Flask application for interactive deepfake image detection via a web interface.

Requirements

• Python 3.x
• PyTorch
• torchvision
• Flask
• NumPy
• Matplotlib
• Jupyter Notebook (optional, for training visualization)

Installation

1. Clone the repository:

   git clone https://github.com/saikrishna823/DeepFake_Detection_Using_MesoNet.git
   

2. Create and activate a Python virtual environment:

   python -m venv venv
   source venv/bin/activate  # For Unix/Linux
   venv\Scripts\activate      # For Windows
   

3. Install dependencies:

   pip install -r requirements.txt
   

Usage

1. Data Preparation:

   • Prepare a dataset containing real and deepfake images.
   • Ensure proper labeling and preprocessing of the image dataset.

2. Model Training:

   • Train the MesoNet model using the provided training script.
   • Adjust hyperparameters and training configurations as needed.

3. Model Evaluation:

   • Evaluate the trained model using the provided evaluation script.
   • Analyze performance metrics such as accuracy, precision, recall, etc.

4. Deployment:

   • Deploy the trained model for real-world deepfake image detection.
   • Integrate the model into an application or platform for automated detection.

5. Flask Application:

   • Navigate to the app directory.

   • Run the Flask application:

     flask run
     

   • Access the deepfake image detection web interface in your browser at http://localhost:5000.

Contributing

Contributions are welcome! Please follow the standard GitHub workflow:

1. Fork the repository.
2. Create a new branch
3. Make your changes.
4. Commit your changes
5. Push to the branch
6. Create a new Pull Request.

Acknowledgements

• Dataset used for training: https://zenodo.org/record/5528418#.YpdlS2hBzDd.

Contact

For inquiries or support, please contact:[email protected].