DeepMirror

DeepMirror is an AI-powered platform for deepfake generation and detection.
The project integrates modern technologies like Flask, Python, HTML/CSS, and Java to provide a user-friendly web interface for creating and analyzing deepfake content.

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🎥 Project Presentation

For a detailed overview of the project, you can view our official project slides here:

👉 DeepMirror Project Slides on Canva

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🚀 Features

• Deepfake Generation: Create deepfake videos and images using advanced deep learning models.
• Deepfake Detection: Analyze and detect manipulated media with state-of-the-art detection algorithms.
• Web Interface: User-friendly web frontend designed with HTML, CSS, and Java, based on prototypes created in Figma.
• RESTful API: Provides API endpoints for easy interaction with generation and detection services.

🛠️ Technology Stack

• Programming Languages: Python, Java
• Frameworks: Flask
• Frontend: HTML, CSS
• Design: Figma
• Operating System: GNU/Linux

📂 Project Structure

DeepMirror/
├── app/                  # Flask application source code
│   ├── static/           # Static files (CSS, JS, Images)
│   ├── templates/        # HTML templates
│   └── routes.py         # Flask routes and application logic
├── models/               # Deep learning models for generation and detection
├── tests/                # Unit tests
├── requirements.txt      # Required Python packages
└── README.md             # Project documentation

⚙️ Installation

1. Clone the repository:

git clone https://github.com/KoaBou/DeepMirror.git
cd DeepMirror

2. Create a virtual environment and install dependencies:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

3. Run the application:

python app/routes.py

4. Access the web app:

Open your browser and go to http://localhost:5000

🧪 Running Tests

To run unit tests:

pytest tests/

🤝 Contributing

We welcome contributions from the community!
Please open a pull request or create an issue to suggest improvements or report bugs.

📄 License

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

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📢 Notes:

• Make sure you also prepare a requirements.txt listing all libraries (Flask, PyTorch, etc.).
• You can expand this README later with example screenshots or API documentation if needed.

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Would you like me to also help you generate a professional example of a screenshot section (with fake images placeholders) to make your GitHub repo look even better? 🚀
If yes, just reply: "Yes, add example screenshots!"!