An advanced deepfake detection system capable of analyzing and identifying manipulated audio, images, and videos using state-of-the-art machine learning techniques.
- Multi-Format Support: Detects deepfakes across various file types:
- Audio files
- Images
- Videos
- URL submissions
- Automated Learning: Implements feedback loop for continuous model improvement
- Law Enforcement Integration: Direct reporting feature to Indian law enforcement agencies
- Analytics Dashboard: Visual analytics for detailed insights and statistics
- Real-time Processing: Optimized for efficient detection and analysis
- Python
- JavaScript
- PyTorch
- TensorFlow
- Keras
- scikit-learn
- NumPy
- pandas
- Matplotlib
- MFCC (Mel-frequency cepstral coefficients)
- Support Vector Machine (SVM)
- Random Forest
- Multi-Layer Perceptron (MLP)
- XGBoost
- Convolutional Neural Networks (CNN) using TensorFlow/Keras
- Combined CNN & RNN architecture
- ResNext & LSTM architectures
- Utilizes MFCC feature extraction
- SVM classifier for genuine vs deepfake classification
- ResNext architecture for spatial feature extraction
- LSTM for temporal sequence analysis
- CNN-based architecture optimized for manipulation detection
- Transfer learning for improved accuracy
-
Data Quality
- Limited availability of labeled deepfake datasets
- Data consistency issues
-
Generalization
- Model adaptation to new types of deepfakes
- Performance on unseen data
-
Performance
- Processing latency for large files
- High computational requirements
-
Data Quality
- Synthetic data generation
- Advanced data augmentation techniques
-
Generalization
- Transfer learning implementation
- Regular model retraining
-
Performance
- Model quantization
- Cloud computing integration
- GPU/TPU acceleration
- Enhanced real-time processing capabilities
- Expanded dataset coverage
- Advanced adversarial training
- Mobile platform support
- API integration options
- Deepfake and Real Images Dataset
- Celeb-DeepFakeForensics
- Deepfake Detection Challenge Dataset
- FaceForensics
-
Vishal Chand
- Email: [email protected]
-
Aditya Singh
- Email: [email protected]
This project is licensed under the MIT License - see the LICENSE.md file for details.
Note: This project was developed as part of the Territorial Army Cyber Challenge: Terrier Cyber Quest 2024 Datathon - Track 3