# Detection of Copy-move Image forgery using SVD and Cuckoo Search Algorithm

## TL;DR

This paper introduces a novel copy-move forgery detection method that combines SVD with the Cuckoo Search algorithm to optimize parameters for improved accuracy across diverse images.

## Contribution

It proposes a new approach integrating Cuckoo Search with SVD for adaptive parameter selection in copy-move forgery detection.

## Key findings

- Enhanced detection accuracy across various images
- Automated parameter optimization using Cuckoo Search
- Outperforms traditional SVD-based methods

## Abstract

Copy-move forgery is one of the simple and effective operations to create forged images. Recently, techniques based on singular value decomposition (SVD) are widely used to detect copy-move forgery (CMF). Some approaches based on SVD are most acceptable to detect copy-move forgery but some copy-move forgery detection approaches can not produce satisfactory detection results. Sometimes these approaches may even produce error results. According to our observation, detection result produced using SVD depend highly on those parameters whose values are often determined with experiences. These values are only applicable to a few images, which limit their application. To solve this problem, a novel approach named as copy-move forgery detection using Cuckoo search algorithm (CMFD-CS) is proposed in this paper. CMFD-CS integrates the CS algorithm into SVD. It utilizes the CS algorithm to generate customized parameter values for images, which are used CMFD under block-based framework.