🔍 Deepfake Detection Models

Advanced Face Anti-Spoofing & Deepfake Detection using Machine Learning, Deep Learning, and GANs

📖 Overview

As synthetic media generation becomes increasingly sophisticated, the need for robust Deepfake Detection mechanisms is more critical than ever. This repository presents a comprehensive evaluation of various algorithms—from classical Machine Learning to state-of-the-art Deep Learning—for identifying deepfakes and spoofed facial images.

The project evaluates architectures across multiple datasets to provide a holistic view of what works best for facial security in real-world scenarios.

🎯 Aim & Motive

As a passionate student and a rapid learner with a strong interest in Artificial Intelligence, I developed this project to showcase my progression and proficiency in Machine Learning.

My goal was not just to use out-of-the-box solutions, but to demonstrate a deep understanding of model evolution:

• Starting from the Basics: Implementing foundational Machine Learning algorithms (SVM, Random Forest, XGBoost) to understand feature extraction and baseline metrics.
• Scaling to the Maximum: Progressing to highly complex Deep Learning architectures (FaceNet, DenseNet) and advanced Ensembles to push accuracy to the limit.

This repository serves as a testament to my ability to research, implement, and evaluate a wide spectrum of algorithms to solve a complex, real-world security problem.

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📂 Project Structure

To maintain a clean and modular codebase, the models and data are organized systematically:

Deepfake-Detection/
│
├── assets/                  # Images and banners for README
├── datasets/                # Dataset loaders and raw files
│   └── nuaa_dataset/        # NUAA Imposter Database
│
├── evaluation/              # Flattened testing and evaluation scripts
│
├── models/                  # Core Models Directory
│   ├── machine_learning/    # SVM, Random Forest, XGBoost
│   ├── deep_learning/       # FaceNet, DenseNet, DNN, HuggingFace integration
│   ├── ensemble/            # Advanced model blending techniques
│   └── gan/                 # Generative Adversarial Network based models
│
└── README.md                # Project documentation

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📊 Datasets Used

This project leverages industry-standard datasets to benchmark the models:

1. NUAA Imposter Database

   • Focuses on 2D face spoofing attacks (print attacks).
   • Contains real face images and corresponding spoofed images captured under various lighting conditions.

2. CelebA-Spoof

   • A large-scale face anti-spoofing dataset.
   • Contains diverse spoofing types including print, replay, and 3D mask attacks.

(Note: Raw large dataset files are not tracked in this repository. Please download them from their respective sources and place them in the datasets/ directory).

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🧠 Model Architectures

We experimented with multiple paradigms to determine the most effective deepfake detection strategy.

1. Classical Machine Learning

Effective for engineered features and tabular representations of image data.

• Support Vector Machines (SVM)
• Random Forest
• XGBoost

2. Deep Learning

Leveraging Convolutional Neural Networks (CNNs) for automatic feature extraction.

• Deep Neural Networks (DNN)
• DenseNet
• FaceNet
• HuggingFace Pre-trained Models

3. Ensemble Methods

Combining predictions from multiple models (e.g., SVM + XGBoost) to achieve higher accuracy and reduce variance.

4. Generative Adversarial Networks (GANs)

Exploring adversarial techniques to detect sophisticated generated media.

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🏆 Results & Accuracy

Our models achieved highly competitive results across various benchmarks. Below are some of the key accuracy metrics extracted from the experiments:

Model Architecture	Dataset	Accuracy	Notes
FaceNet + DenseNet	CelebA / Mixed	99.96%	Exceptional feature extraction; best performing Deep Learning model.
Random Forest	NUAA	98.97%	High precision (99.71%) on print attacks.
Hybrid Ensemble (SVM + XGB)	CelebA / Mixed	98.33%	Best performing traditional ML ensemble.
Support Vector Machine (SVM)	CelebA / Mixed	98.27%	Strong baseline performance.
XGBoost	CelebA / Mixed	98.14%	Fast and highly accurate.

(For detailed confusion matrices and classification reports, please refer to the specific .ipynb files in the models/ directory).

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🚀 Getting Started

Prerequisites

Ensure you have Python 3.8+ installed. You can install the required dependencies using:

pip install -r requirements.txt

Running the Notebooks

1. Clone the repository:

   git clone https://github.com/YourUsername/Deepfake-Detection.git
   cd Deepfake-Detection

2. Navigate to your desired model directory:

   cd models/machine_learning

3. Launch Jupyter Notebook:

   jupyter notebook

4. Open any .ipynb file and run the cells sequentially.

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🤝 Contributing

Contributions are what make the open-source community such an amazing place to learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

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📝 License

Distributed under the MIT License. See LICENSE for more information.

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