ayush200399391001/Video_detection_and_manipulation

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# Video Manipulation Detection Using AI

This project uses an AI-powered video analysis pipeline to detect potential video manipulations such as deepfakes and other visual tampering. It leverages the **DEtection TRansformer (DETR)** model for object detection and optical flow analysis to identify motion inconsistencies. The application is built using **Streamlit**, and the model processes videos to generate detailed reports on their authenticity.

## Features
- **Metadata Extraction**: Extracts and displays metadata from uploaded videos.
- **Frame Extraction**: Extracts frames from videos at specified intervals for analysis.
- **Object Detection**: Analyzes each frame to detect objects using the DETR model.
- **Optical Flow Calculation**: Computes motion patterns to detect inconsistencies.
- **Report Generation**: Compiles visual and motion analysis results into a comprehensive JSON report.
- **Manipulation Detection**: Uses the report to assess the likelihood of video manipulation.

## Technologies Used
- **Streamlit**: Front-end web framework for creating the user interface.
- **PyTorch**: Machine learning framework used for object detection (DETR).
- **OpenCV**: Library for video and image processing (frame extraction, optical flow).
- **Transformers**: Pre-trained DETR model for object detection.
- **FFmpeg**: Tool for extracting metadata from videos and processing video files.

## Installation

1. Clone the repository:
   ```bash
   git clone https://github.com/your-username/video-manipulation-detection.git
   cd video-manipulation-detection

2. Install dependencies:

   pip install -r requirements.txt
   

3. Run the Streamlit app:

   streamlit run app.py
   

How to Use

1. Upload a Video: Go to the "Analyze Video" section and upload a .mp4 or .mov video file.
2. Start Analysis: Click the "Analyze Video" button to start processing.
3. View Results: After analysis, the app will display a score and details about the potential manipulation of the video.

Report Details

The report includes the following information:

• Frame Analysis: Number of detections, maximum and average confidence of the object detection model.
• Motion Analysis: Calculated optical flow values to detect motion inconsistencies.
• Summary Stats: Overall detection confidence, optical flow statistics, and manipulation detection score.

License

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

Acknowledgments

• Streamlit
• PyTorch
• Hugging Face Transformers
• OpenCV
• FFmpeg
• This project is built as part of a research and development effort to analyze and detect video manipulations.

Contact

• Ayush Agarwal
  AIML Enthusiast, VIT Bhopal University
  📧 [email protected]
  🔗 LinkedIn
  🐙 GitHub

### Key Sections:
1. **Project Overview**: Describes the purpose and features of the app.
2. **Technologies Used**: Lists the libraries and tools used in the project.
3. **Installation**: Step-by-step guide on how to clone, install dependencies, and run the app.
4. **How to Use**: Instructions on how users can interact with the app.
5. **Report Details**: Describes the information generated in the analysis report.
6. **License & Acknowledgments**: Includes the MIT License and credits to the libraries and tools used.

This will help users understand the project and how to deploy and use it. Feel free to customize it further!