https://github.com/tedoaba/yolov5-image-tampering-detection
Image-Tampering-Detection-using-YOLOv5
https://github.com/tedoaba/yolov5-image-tampering-detection
Last synced: 3 months ago
JSON representation
Image-Tampering-Detection-using-YOLOv5
- Host: GitHub
- URL: https://github.com/tedoaba/yolov5-image-tampering-detection
- Owner: tedoaba
- Created: 2024-04-08T07:06:40.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-04-08T07:27:01.000Z (about 1 year ago)
- Last Synced: 2024-04-08T08:28:01.299Z (about 1 year ago)
- Language: Jupyter Notebook
- Size: 10.7 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Image Tampering Recognition using YOLOv5
This repository contains an implementation of image tampering recognition using YOLOv5, based on the paper ["Image Tampering Recognition Algorithm using Improved YOLOv5"](https://ieeexplore.ieee.org/document/10238704) by Z. Liu.
## Paper Information
- **Title**: Image Tampering Recognition Algorithm using Improved YOLOv5
- **Paper Link**: [IEEE Xplore](https://ieeexplore.ieee.org/document/10238704)
- **Citation**: Z. Liu, "Image Tampering Recognition Algorithm Based on Improved YOLOv5s," in IEEE Access, vol. 11, pp. 95114-95119, 2023, doi: 10.1109/ACCESS.2023.3311474.
- **Keywords**: Feature extraction, Image recognition, Neck, Prediction algorithms, Data augmentation, Object detection, Ethics, Biomedical image processing, Image tamper recognition, YOLOv5s, attention module, EIOU loss function
## Installation
To use this implementation, you need to follow these steps:
1. Clone the YOLOv5 model repository:
2. Install the required packages:
3. Install Roboflow:
4. Install ClearML for visualization:
## Dataset
The dataset used for this implementation is obtained from Roboflow Universe. You can find the dataset [here](https://universe.roboflow.com/pavan-kumar/forge-eq4rh).
- Total Images: 7257
- Train: 5075
- Valid: 1459
- Test: 723
## Customization
To adapt the YOLOv5 architecture for image tampering recognition, the number of classes has been modified from 80 to 2 based on the dataset.
## Usage
1. **Training**: Use the provided dataset to train the YOLOv5 model. You can train the model using the following command:
2. **Evaluation**: After training, evaluate the model's performance using the validation dataset:
3. **Testing**: Finally, test the trained model using the test dataset:
Feel free to explore and modify the code to suit your specific requirements.
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.