Enhancing Voice Authentication Security: A Hybrid Approach Using GMM-UBM and Deep4SNet for Deepfake Detection
This project introduces a robust voice authentication system designed to counter synthetic audio attacks. By integrating advanced deepfake detection and speaker verification technologies, it ensures high accuracy, real-time performance, and scalability for applications like secure banking, smart devices, and access control systems.
- Dual-Stage Security:
- Combines deepfake detection and speaker verification for enhanced reliability.
- High Detection Accuracy:
- Deepfake Detection: 90.25% accuracy against synthetic audio attacks.
- Speaker Verification: 100% accuracy for enrolled users with zero false acceptances.
- Real-Time Processing: Low computational overhead, ensuring seamless integration with various systems.
- Scalability: Easily adaptable to a wide range of applications and environments.
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Deepfake Detection
- Model: Deep4SNet, a specialized neural network for identifying synthetic audio.
- Features: Spectrogram-based analysis for binary classification (real vs. fake audio).
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Speaker Verification
- Model: GMM-UBM (Gaussian Mixture Model - Universal Background Model).
- Features: MFCC (Mel-Frequency Cepstral Coefficients) extraction to create robust, speaker-specific models.
Ensure you have the following installed:
- Python: Version 3.8 or higher.
- Libraries: Refer to the
requirements.txtfile for all dependencies.
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Clone the Repository
git clone https://github.com/ananyakaligal/Voice-Authentication.git cd voice-authentication-research -
Set Up a Virtual Environment
python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate
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Install Dependencies
pip install -r requirements.txt
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Start the System
python main.py
Contributions are welcome! If you encounter any issues, have suggestions for improvements, or want to add new features, feel free to open an issue or submit a pull request. Here’s how you can contribute:
- Fork the repository to your own GitHub account.
- Create a new branch for your feature or bug fix.
- Push your changes to your forked repository.
- Open a pull request with a clear description of your changes.
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Adaptive Learning Against Various Threats:
Ongoing model training employing large numbers of synthetic audio attacks, including state-of-the-art DeepFAKE methods, to ensure the viability of the system in progressive dangers, with a focus on voice-oriented features. -
Actual Field Deployment and Testing:
Full-scale development of actual field trials in several diverse environments, including noisy or uncontrolled conditions, to better simulate how a voice-dependent authentication system is used under real-world scenarios. -
Integration of Multi-Layered Security:
Augmenting the framework to incorporate various layers of security, including the amalgamation of biometric characteristics (such as facial recognition) with voice authentication, to establish a multi-modal authentication system that bolsters overall security. -
Model Efficiency and Scalability:
Further optimizing the Deep4SNet model to reduce computational overhead without sacrificing accuracy, enabling seamless deployment in resource-constrained devices or applications, especially those relying on voice-dependent authentication. -
Integration with Other Voice-based Systems:
The extension of the hybrid framework for the inclusion of more voice-based applications like virtual assistants and smart home devices, improving security in more platforms requiring voice-dependent authentication. -
User-Specific Voice Adaptation:
Developing mechanisms for continuous, personalized voice profile updates to improve system accuracy as user voice characteristics naturally evolve over time, ensuring sustained voice-dependent accuracy.
We appreciate all contributions, whether they be bug fixes, new features, documentation improvements, or enhancements!
This project is licensed under the MIT License - see the LICENSE file for details.