https://github.com/eyuuab/frame_level_video_forgery_detaction
A Python project for detecting video tampering using frame difference analysis with OpenCV and NumPy. Includes anomaly detection and visualization tools.
https://github.com/eyuuab/frame_level_video_forgery_detaction
computer-vision frame-difference numpy opencv tampering-detection
Last synced: 29 days ago
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A Python project for detecting video tampering using frame difference analysis with OpenCV and NumPy. Includes anomaly detection and visualization tools.
- Host: GitHub
- URL: https://github.com/eyuuab/frame_level_video_forgery_detaction
- Owner: eyuuab
- Created: 2025-01-22T17:18:52.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-23T19:32:13.000Z (4 months ago)
- Last Synced: 2025-03-30T01:23:39.962Z (about 2 months ago)
- Topics: computer-vision, frame-difference, numpy, opencv, tampering-detection
- Language: Jupyter Notebook
- Homepage:
- Size: 80.1 KB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Video Tampering Detection
This project analyzes video frame differences to detect potential tampering, such as duplicate or frozen frames. Using OpenCV and NumPy, the script compares a tampered video with a real video and visualizes the results.
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## Method
The script uses **frame difference analysis**:
1. Convert each video frame to grayscale to simplify processing.
2. Calculate the absolute difference between consecutive frames.
3. Count the number of non-zero pixels in the difference to measure changes.
4. Flag frames with minimal changes (below a threshold) as potential duplicates or frozen frames.
This approach highlights anomalies that may indicate video tampering.
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## Requirements
- Python 3.x
- Required Libraries:
- OpenCV (`cv2`)
- NumPy (`numpy`)
- Matplotlib (`matplotlib`)
Install dependencies using:
```bash
pip install opencv-python-headless numpy matplotlib