An AI-powered Deepfake Detection Framework focused on detecting manipulated facial media using deep learning, computer vision, forensic analysis, and federated learning concepts.
The project is designed for research, cyber forensics investigation, media integrity validation, and real-time deepfake analysis workflows.
- Deepfake image and video detection
- AI-based facial forgery analysis
- CNN and transfer learning based classification
- Federated learning experimentation support
- Real-time media analysis workflow
- Face extraction and preprocessing pipeline
- Deepfake probability scoring
- Forensic-oriented analysis pipeline
- Dockerized deployment support
- Research-focused modular architecture
- Python
- TensorFlow / PyTorch
- OpenCV
- FastAPI / Flask
- CNN
- ResNet50
- Federated Learning
- Docker
- Streamlit
- NumPy
- Pandas
Deepfake-AFDF/
├── datasets/
├── models/
├── notebooks/
├── api/
├── frontend/
├── docker/
├── utils/
├── training/
├── inference/
├── requirements.txt
├── Dockerfile
└── README.md
Deepfake-AFDF is developed to identify manipulated facial media generated using AI-based synthesis techniques such as:
- Face Swap
- Face Reenactment
- GAN-generated media
- AI-generated synthetic faces
- Facial manipulation attacks
The framework focuses on combining:
- Deep learning
- Computer vision
- forensic validation
- explainable AI concepts
- federated learning workflows
to build scalable and research-oriented detection systems.
- Upload image or video
- Extract facial regions
- Perform preprocessing and normalization
- Generate deep feature embeddings
- Run AI-based forgery classification
- Compute deepfake confidence score
- Generate forensic analysis results
- Return detection output and visualization
- Deepfake Detection
- Facial Forgery Analysis
- Media Forensics
- Explainable AI (XAI)
- Federated Learning
- Privacy-Preserving AI
- Adversarial Attack Analysis
- AI Security
- Digital Evidence Validation
git clone git@github.com:code-with-nc/Deepfake-AFDF.git
cd Deepfake-AFDFpip install -r requirements.txtpython app.pyor
uvicorn app:app --reloaddocker build -t deepfake-afdf .docker run -p 8000:8000 deepfake-afdfThe framework may use:
- CNN-based classifiers
- ResNet50
- Transfer Learning
- Image Feature Extraction
- Frame-based Video Analysis
- Ensemble Detection Approaches
for identifying manipulated media artifacts.
Compatible with datasets such as:
- FaceForensics++
- Celeb-DF
- DFDC
- DeepFake-TIMIT
- Custom forensic datasets
- Cyber Forensics Investigation
- Fake Media Detection
- Social Media Verification
- Digital Evidence Validation
- Research & Academic Projects
- AI Security Testing
- Threat Intelligence Workflows
- Media Authenticity Analysis
This repository is intended strictly for:
- educational use
- authorized research
- forensic investigation
- media authenticity validation
The framework must not be used for malicious media manipulation, identity misuse, misinformation generation, or unauthorized surveillance.
- Celeb-DF Dataset
- FaceForensics++
- DeepFake Detection Research
- Federated Learning Research
- Adversarial AI Security Papers
- Explainable AI visualization
- Real-time webcam detection
- Blockchain-based evidence validation
- Differential privacy integration
- Federated aggregation server
- Multi-modal deepfake detection
- Audio-video forgery analysis
Narayani
GitHub: code-with-nc
This repository is developed for academic research, cyber security education, digital forensics investigation, and responsible AI research only.