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Deepfake Detection Web Application

A web application built to detect deepfake images and videos using machine learning models. This project aims to provide users with an easy-to-use interface for uploading media files and receiving predictions on whether the content is a deepfake or authentic.

Table of Contents

Overview

This application uses a custom-trained deep learning model to classify images and videos as deepfake or authentic. Users can upload media files through a frontend interface, which sends the files to a backend server for analysis. The backend then processes the file using a convolutional neural network (CNN) and returns the results to the user.

Features

  • Deepfake Detection: Detects whether an uploaded image or video is a deepfake.
  • User Authentication: Secure login and signup functionality.
  • Upload and View History: Users can upload images and view past results.
  • Confidence Score: The model provides a confidence score indicating the likelihood that a media file is a deepfake.

Technologies Used

  • Frontend: React.js, Tailwind CSS
  • Backend: Flask (Python)
  • Machine Learning: TensorFlow, Keras (for CNN model)
  • Firebase: Authentication and storage
  • Cloud Services: Firebase, or local Flask API for processing media files

Project Structure

deepfake-detection
├── models                  # ML models (e.g., image_model.h5)
├── public                  # Public assets for React app
├── src
│   ├── components          # React components
│   ├── firebase            # Firebase configuration files (if using Firebase)
│   ├── pages               # Main pages (Home, Dashboard, etc.)
│   ├── App.js              # Main app entry point
│   ├── index.js            # React root
│   └── ...                 # Other frontend files
├── app.py                  # Flask backend server
├── README.md               # Project documentation
└── ...                     # Other configuration files (package.json, etc.)

Installation and Setup

Prerequisites

  • Node.js and npm installed for the React frontend.
  • Python and pip for the Flask backend.

Frontend Setup (React)

  1. Clone the repository: 
    git clone https://github.com/lakshyagrg23/DeepfakeDetection.git
cd DeepfakeDetection
  2. Navigate to the React app directory: 
    cd src
  3. Install dependencies: 
    npm install
  4. Start the React development server: 
    npm start

The React app should now be running at http://localhost:3000.

Backend Setup (Flask)

  1. Install Python dependencies (ensure you're in the root project directory): 
    pip install -r requirements.txt
  2. Run the Flask server: 
    python app.py

The Flask server should now be running at http://127.0.0.1:5000.

  1. If you're using Firebase for authentication or storage, configure Firebase by adding your Firebase configuration to the appropriate files.

Environment Variables

Create a .env file in the root directory to store any necessary environment variables, such as Firebase configuration keys or Flask secret keys.

Usage

  • Sign Up / Log In: Register or log in using the authentication system.
  • Upload Media: Choose to upload an image or video for deepfake detection.
  • View Results: After uploading, the system will analyze the media and return a prediction indicating whether it’s likely a deepfake, along with a confidence score.
  • History: View past uploads and results in the History section.

License

This project is licensed under the MIT License. See the LICENSE file for details.