The Deepfake Audio Detection System is a robust solution designed to identify synthetic audio generated by AI. Utilizing advanced neural networks, blockchain verification, and secure encryption, this project aims to ensure the authenticity and integrity of voice recordings.
- 98.2% Accurate Detection: Advanced neural networks to detect synthetic voices.
- Real-Time Analysis: Processes audio in under 100ms.
- Blockchain Verification: Tamper-proof authenticity records.
- Enterprise Security: End-to-end encrypted processing.
- Comprehensive API: Seamless integration with existing systems.
| Component | Technology |
|---|---|
| Frontend | React, TypeScript, Tailwind CSS |
| Backend | Node.js, TensorFlow, Python |
| Security | AES-256 Encryption, Blockchain Anchoring |
| DevOps | Docker, Kubernetes, GitHub Actions |
- frontend/ # React-based user interface
- backend/ # Node.js & TensorFlow backend API
- models/ # Pre-trained deep learning models
- blockchain/ # Blockchain integration scripts
- scripts/ # Utility scripts for data processing
- Node.js and npm
- Python 3.x
- Docker and Kubernetes
- TensorFlow library
- Clone the repository:
git clone https://github.com/yourusername/deepfake-audio-detection.git- Install dependencies:
cd backend
pip install -r requirements.txt
cd ../frontend
npm install- Run the application:
docker-compose up --build- Upload an audio file to analyze its authenticity.
- Get real-time results with confidence scores.
- Verify authenticity through blockchain records.
- Pre-trained TensorFlow models are fine-tuned on a large dataset of real and synthetic audio.
- You can retrain the model by running:
python train_model.py- AES-256 encryption secures all data transfers.
- Blockchain anchoring ensures tamper-proof verification.
Contributions are welcome! Please open an issue or submit a pull request.