Project Workflow
Our deepfake audio detection system follows a comprehensive workflow from data collection to model deployment.
1. Data Collection
Utilizing the ASVspoof2019 dataset
- Source: ASVspoof2019_LA_train
- Format: FLAC audio files
- Classes: Bonafide and Spoof
- Protocol files for labeling
2. Data Preprocessing
Standardized audio processing pipeline
- Audio loading and resampling
- Mel spectrogram extraction
- Feature normalization
- Data augmentation
3. Model Development
CNN-based deep learning model
- Architecture design
- Layer configuration
- Hyperparameter tuning
- Model training
4. Evaluation
Comprehensive model assessment
- Training metrics tracking
- Validation performance
- Confusion matrix analysis
- Error analysis
5. Security Measures
Robust security implementation
- Input validation
- Model protection
- Secure file handling
- Access control
Security Features
Input Validation
Strict validation of audio file formats and content
Model Protection
Secure storage and access control for the trained model
Fraud Prevention
Advanced detection of manipulation attempts
Access Control
Role-based access to system features