Image Forgery Detection

Image Forgery Detection Using JPEG compression and Convolutional Neural Network (CNN). This project aims to detect image forgery using JPEG compression and Convolutional Neural Network (CNN). The project is implemented in Python 3.9.13 and Tensorflow 2.10 . The dataset used is the CASIA2 dataset.

Table of Contents

• Installation
  • Prerequisites
  • Installation
• Contributing
• License

Installation

Prerequisites

• Python 3.9.13
• Tensorflow 2.10
• Numpy 1.21.2 or higher
• Matplotlib 3.4.3 or higher
• Scikit-learn 1.0.1 or higher
• Scikit-image 1.0.1 or higher
• Pillow 8.4.0 or higher
• Pandas 1.3.4 or higher
• Jupyter Notebook 6.4.6 or higher
• Streamlit 1.0.0 or higher
• OpenCV 4.5.4 or higher

Installation

1. Clone the repo

   git clone https://github.com/basavbamrah/image-forgery-detection

2. Install Python packages
3. Install Tensorflow
4. Run the Jupyter Notebook file
   • train.ipynb to train the model
   • test.ipynb to test the model
5. Run the Streamlit file
   • streamlit run app.py

Contributing

Contributions are what make the open source community such an amazing place to be learn, inspire, and create. Any contributions you make are greatly appreciated.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.