https://github.com/sam120204/mediashieldai
MediaShieldAI is an advanced tool designed to detect AI-generated images and videos (deepfakes) using sophisticated machine learning techniques. The goal is to create a robust system that can analyze media files, determine their authenticity, and provide confidence scores to users.
https://github.com/sam120204/mediashieldai
Last synced: 9 months ago
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MediaShieldAI is an advanced tool designed to detect AI-generated images and videos (deepfakes) using sophisticated machine learning techniques. The goal is to create a robust system that can analyze media files, determine their authenticity, and provide confidence scores to users.
- Host: GitHub
- URL: https://github.com/sam120204/mediashieldai
- Owner: Sam120204
- License: mit
- Created: 2024-06-21T04:08:25.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-07-13T04:00:36.000Z (over 1 year ago)
- Last Synced: 2025-02-13T00:16:23.351Z (11 months ago)
- Language: Python
- Size: 19.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# MediaShieldAI
**MediaShieldAI** is an advanced tool designed to detect AI-generated images and videos (deepfakes) using sophisticated machine learning techniques. The goal is to create a robust system that can analyze media files, determine their authenticity, and provide confidence scores to users.
## Features
- **Image and Video Upload**: Users can upload both images and videos for analysis.
- **Real-Time Analysis**: The application analyzes the uploaded media files and determines their authenticity.
- **Confidence Score**: The app provides a confidence score indicating the likelihood of the media being fake.
- **User-Friendly Interface**: A simple and intuitive interface for easy user interaction.
## Technology Stack
- **Frontend**: HTML, CSS, JavaScript (for user interface)
- **Backend**: Flask (Python web framework)
- **Machine Learning**: PyTorch (for model training and inference)
- **Deployment**: Cloud services like AWS, Azure, or Heroku
## Project Plan
### Phase 1: Planning and Requirements
- **Define Objectives**: Detect AI-generated images and videos with high accuracy. Provide a user-friendly interface for media upload and analysis. Ensure scalability for real-time detection capabilities.
- **Team Roles**: Data Scientist, Software Engineer, UI/UX Designer.
- **Data Requirements**: Use the ArtiFact dataset from Kaggle, which contains real and fake images and videos.
### Phase 2: Data Collection and Preprocessing
- **Download Dataset**: Access the ArtiFact dataset from Kaggle ([ArtiFact Dataset](https://www.kaggle.com/datasets/awsaf49/artifact-dataset)).
- **Preprocess Images**: Resize, normalize, and augment images.
- **Preprocess Videos**: Extract frames, resize, normalize, and augment video data.
### Phase 3: Model Development
#### For Image Detection
- **Model Selection**: Use pre-trained models like VGG16, ResNet50 for transfer learning.
- **Model Training**: Train the model on the image dataset, validate, and tune hyperparameters.
- **Model Evaluation**: Evaluate using metrics like accuracy, precision, recall, and F1-score.
#### For Video Detection
- **Frame Extraction**: Extract frames from videos.
- **Model Selection**: Use pre-trained models like 3D CNNs for video analysis.
- **Model Training**: Train the model on the video dataset, validate, and tune hyperparameters.
- **Model Evaluation**: Evaluate using metrics like accuracy, precision, recall, and F1-score.
### Phase 4: Backend and Frontend Development
#### Backend
- Set up a Flask server.
- Implement API endpoints for image and video upload and analysis.
- Integrate the trained models for inference.
#### Frontend
- Design a simple web interface for users to upload images and videos.
- Display analysis results and confidence scores.
- Ensure responsive design for various devices.
### Phase 5: Testing and Deployment
#### Testing
- Perform unit and integration testing.
- Conduct user testing to gather feedback on usability and performance.
#### Deployment
- Deploy the application on a cloud platform.
- Set up a CI/CD pipeline for continuous integration and deployment.
- Monitor application performance and resolve any issues.
### Phase 6: Future Enhancements
- **Extend the Model**: Handle real-time video data and optimize for faster processing.
- **Advanced Features**: Add batch analysis, detailed reports, and integration with social media platforms.