Unified-DeepFake-Detector

A unified deepfake detection framework for both image and video analysis. This repository includes a TensorFlow-based model leveraging the Xception architecture for shared feature extraction, combined with LSTM for temporal video processing.

Key Features

• Unified architecture for both image and video deepfake detection
• Based on Xception backbone with additional LSTM layers for temporal analysis
• Supports both single-frame and multi-frame inputs
• Comprehensive logging and model checkpointing
• Modular design for easy maintenance and extension

Project Structure

unified-deepfake-detection/
├── com/
│   └── mhire/
│       ├── data_processing/
│       │   └── data_processing.py
│       ├── training/
│       │   └── training.py
│       └── evaluation/
│           └── evaluation.py
├── main.py
├── requirements.txt
├── README.md
└── LICENSE

Component Description

• data_processing.py: Handles data loading, preprocessing, and dataset splitting
• training.py: Contains the model architecture and training pipeline
• evaluation.py: Manages model evaluation and metrics calculation
• main.py: Entry point of the application, orchestrates the entire pipeline

Technical Architecture

The system uses a dual-branch architecture:

1. Image Branch: Processes single frames using Xception backbone
2. Video Branch: Processes sequences using Xception + LSTM for temporal features
3. Unified Output: Combines both branches for comprehensive detection

Installation

1. Clone the repository:

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

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Dataset Structure

The system expects data in the following structure:

dataset/
├── real/
│   ├── video1/
│   │   ├── frame001.png
│   │   ├── frame002.png
│   │   └── ...
│   └── ...
└── fake/
    ├── video1/
    │   ├── frame001.png
    │   ├── frame002.png
    │   └── ...
    └── ...

Usage

1. Update the paths in main.py:

base_dir = Path("your/base/directory")
data_path = base_dir / "Datasets/FaceForensics"
processed_data_dir = base_dir / "ProcessedData/FaceForensics"
model_dir = base_dir / "Trained_models/Unified_DeepFake_Detection_Model"

2. Run the pipeline:

python main.py

The system will:

1. Process and split the dataset (70% train, 15% validation, 15% test)
2. Train the unified model
3. Evaluate performance and generate metrics

GPU Support

The system automatically detects and utilizes available GPU resources. For optimal performance:

• CUDA-compatible GPU recommended
• Minimum 8GB GPU memory
• Updated GPU drivers

Troubleshooting

Common issues and solutions:

1. GPU Memory Error:

   • Reduce batch size in training.py
   • Decrease image size or sequence length

2. Data Loading Error:

   • Verify dataset structure
   • Check file permissions
   • Ensure correct path configuration

3. Training Instability:

   • Adjust learning rate
   • Modify batch size
   • Check for data imbalance

Contributing

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

License

This project is licensed under the GNU General Public License v3.0 - see the LICENSE file for details.

Citation

If you use this code in your research, please cite:

@project{unified_deepfake_detection,
  title = {Unified DeepFake Detection System},
  year = {2025},
  author = {Syeda Aunanya Mahmud},
  url = {https://github.com/Aunanya875/Unified-DeepFake-Detector}
}