DeepFake Detection Web Application

Overview

This project is a web-based application for detecting deepfake images and videos using an ensemble of deep learning models. It provides a user-friendly interface for uploading media files, processes them using state-of-the-art deepfake detection algorithms, and presents the results with detailed analysis and visualization.

Features

• File Upload: Supports drag-and-drop or button-click file upload for images and videos.
• DeepFake Detection: Utilizes an ensemble of EfficientNet-B7 models for robust deepfake detection.
• Detailed Analysis: Provides a comprehensive report on the authenticity of the uploaded media.
• Visualization: Includes donut charts for overall confidence and bar charts for specific metrics.
• Responsive Design: Works seamlessly on desktop and mobile devices.
• Dark/Light Mode: Supports theme switching for user preference.

Project Images

Technology Stack

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript
• Machine Learning: PyTorch, timm (PyTorch Image Models)
• Data Visualization: Matplotlib, Plotly
• AI-Generated Reports: Google's Gemini API

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/deepfake-detection-app.git
   cd deepfake-detection-app
   

2. Set up a virtual environment (optional but recommended);

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

3. Install the required packages:

   pip install -r requirements.txt
   

4. Download the model weights and place them in the weights directory.

5. Set up your Gemini API key:

   • Create a .env file in the root directory
   • Add your API key: GEMINI_API_KEY=your_api_key_here

Usage

1. Start the Flask server:

   python app.py
   

2. Open a web browser and navigate to http://localhost:5000.

3. Upload an image or video file using the interface.

4. View the detection results, including the confidence score, detailed report, and visualizations.

Project Structure

• app.py: Main Flask application file
• deepfake_model.py: DeepFake detection model implementation
• static/: Contains CSS and JavaScript files
• styles.css: Main stylesheet
• index.js: Frontend JavaScript logic
• templates/: Contains HTML templates
• index.html: Main page template
• weights/: Directory for storing model weights (not included in repository)

Contributing

Contributions to improve the application are welcome. Please follow these steps:

1. Fork the repository
2. Create a new branch (git checkout -b feature-branch)
3. Make your changes and commit them (git commit -am 'Add some feature')
4. Push to the branch (git push origin feature-branch)
5. Create a new Pull Request