Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/aayushiahlawat/image-forgery-detection

System to detect Copy-Move forgery using Python and machine learning techniques
https://github.com/aayushiahlawat/image-forgery-detection

anaconda forgery image machine-learning python rsa-algorithm svm-classifier

Last synced: 6 days ago
JSON representation

System to detect Copy-Move forgery using Python and machine learning techniques

Awesome Lists containing this project

README 

        
# Image Forgery Detection



  Python



## Overview 🔍



In the project "Image Forgery Detection," I developed a robust system aimed at detecting Copy-Move forgery, a commonly employed technique in image manipulation. 



### Objective 🎯



The purpose of choosing this project is:



- **Digital Images Forensics (DIF):** Vanguard of security techniques aiming at restoration of lost trust in digital imagery by exposing digital forgery techniques.

- **Existing Techniques:** Explore active and passive (blind) approaches in image forgery detection.

- **Validation:** Validate the originality of digital images by recovering information about their history.

- **Trust Building:** Analyze images under specific conditions to build trust and genuineness.



### Methodology 🛠️



The proposed system utilizes SVM classifier for forgery detection, employing hashing techniques and RSA key encryption for security. The methodology involves two main phases: training and testing.



1. **Training Phase:**

   - **Database Creation:** A database of images is created for training purposes. Images are sourced from various online repositories or captured using digital cameras. Images can vary in size and format (jpg, jpeg).

   - **RSA Key:** An RSA key is generated after training images are ingested into the system. During testing, users are prompted to enter a consistent key to ensure authorized access.

   - **Pre-processing:** Images undergo pre-processing steps such as conversion to grayscale from RGB, noise removal using median filtering, and enhancement techniques like histogram equalization and sharpening.

   - **Feature Extraction:** Various image features are extracted including:

     - **Pixel Analysis:** Calculation of mean and standard deviation of pixel values.

     - **Texture Analysis:** GLCM (Gray-Level Co-occurrence Matrix) analysis for texture representation using Haralick functions.

   - **Hash Values:** Hash values are computed for the extracted features to facilitate efficient comparison and identification of duplicated or manipulated regions within images.

   - **SVM Classifier:** Support Vector Machine (SVM) classifier is trained using labeled datasets to establish decision boundaries and identify fraudulent image regions with high precision.



2. **Testing Phase:**

   - **Input Query Image:** Users provide a query image to be authenticated.

   - **RSA Key Authentication:** Users are prompted to enter the consistent RSA key generated during training for authentication.

   - **Pre-processing and Feature Extraction:** Similar pre-processing and feature extraction steps are performed on the query image.

   - **Hash Values Calculation:** Hash values are computed for the extracted features of the query image.

   - **SVM Classification:** SVM classifier is utilized to classify the query image based on the decision boundaries established during training.



### Results 📊



- **High Accuracy:** Achieved a remarkable 95% accuracy rate in identifying forged images, showcasing the robustness and reliability of the detection algorithms.

- **Effective Detection:** Successfully detected instances of Copy-Move forgery, a challenging form of image manipulation commonly employed to deceive viewers.



## Getting Started 🚀



To get started with the project, follow these steps:



1. Clone the repository: `git clone https://github.com/AayushiAhlawat/Image-Forgery-Detection.git`

2. Run the main script: `python Implementation.py`



### Conclusion 🎉



The Image Forgery Detection project demonstrates the effectiveness of machine learning techniques, specifically SVM classifiers, in identifying instances of digital image forgery. With a focus on Copy-Move forgery, the system achieves high accuracy rates and provides a reliable solution for image authenticity verification.