DeepFake Detection

DeepFake Detection is a computer vision project that focuses on face extraction, recognition model training, and face prediction in both images and videos.

Notebooks Overview

1. Face Image Extraction (FaceImageExtraction.ipynb)

• Extracts faces from images
• Preprocesses facial data for training
• Creates datasets from image collections
• Outputs cropped/aligned face images

2. Model Training & Evaluation (ModelTrainandEval.ipynb)

• Implements face recognition model architecture
• Trains models using extracted face datasets
• Evaluates model performance metrics
• Contains validation and testing workflows
• Includes model saving functionality

3. Prediction (Predection.ipynb)

• Loads trained models for inference
• Performs face recognition on new images
• Generates predictions with confidence scores
• Visualizes recognition results

4. Video Processing (VideoProcessing.ipynb)

• Processes video files frame-by-frame
• Applies face detection on video streams
• Implements real-time recognition
• Outputs annotated video with recognition results

Getting Started

1. Clone the repository:

git clone https://github.com/MLNexusHub/DeepFake_Detection.git

2. Run Jupyter notebooks in this order:

1. FaceImageExtraction.ipynb
2. ModelTrainandEval.ipynb
3. Predection.ipynb (for images)
4. VideoProcessing.ipynb (for videos)

Project Structure

DeepFake_Detection/
├── FaceImageExtraction.ipynb
├── ModelTrainandEval.ipynb
├── Predection.ipynb
├── VideoProcessing.ipynb
├── 1_scrrenshot.jpg
├── .gitignore
├── LICENSE
└── README.md

Requirements

• Python 3.7+
• OpenCV
• TensorFlow/Keras
• dlib
• face_recognition
• Jupyter Notebook
• Other dependencies specified in notebooks

License

This project is licensed under the terms of the MIT License - see LICENSE file for details.

Contribution

Contributions are welcome! Please open an issue or submit a pull request for any improvements.