Overview

This application is a comprehensive platform for detecting and analyzing media authenticity. It supports various media types including images, videos, and audio files. The application utilizes advanced algorithms and models for deepfake detection in videos, authenticity checks in images, and integrity verification in audio files.

Features

1. Media Upload and Storage: Securely upload and store images, videos, and audio files.
2. Deepfake Detection in Videos: Uses advanced algorithms to detect deepfakes in videos.
3. Image Forgery Detection: Analyzes images for signs of manipulation.
4. Audio Analysis: Checks audio files for authenticity and signs of tampering.
5. User Authentication: Secure sign-in and sign-up features with Firebase Authentication.
6. Dashboard: View analytics and results of media analysis.

Demo

Demo Video of the project: Video Link

Model Weights

For model weights, contact Ananya Gupta

Installation

1. Clone the Repository

   git clone https://github.com/Ananya2003Gupta/DeepFakeDetection.git
   

2. Install Dependencies

   Navigate to the project directory (website) and install the required Python packages.

   pip install -r requirements.txt

3. Set Environment Variables

   Set up necessary environment variables for Firebase services.

4. Run the Application

   python main.py
   

Usage

1. Start the Application:

   After installation, run the application. The service will start on the default Flask port.

2. Navigate to the Application:

   Open a web browser and go to http://localhost:5000 to access the application.

3. Register/Login:

   Create a new account or log in to access the dashboard.

4. Upload Media:

   Use the upload interface to submit images, videos, or audio files for analysis.

5. View Results:

   Analyzed results will be displayed on the dashboard, along with relevant statistics and information.