Image Copy-move Forgery Detection and Classification Using Golden Jackal Optimization Based Multi-Support Vector Machine

Abstract

Protecting the data against forgery is an important concept and digital images are necessary for exhibiting information. Digital image forgeries are attaching extraordinary patterns to original images and it causes visual heterogeneousness. Image copy-move forgery is a challenging technique, that involves copying part of an image and then pasting the copied part into the same image. In this paper, the golden jackal optimization (GJO) is proposed for feature selection and multi-support vector machine (M-SVM) is proposed for effective classification. The GJO optimizes the feature selection process by iteratively searching for the most informative subset of features. By leveraging the exploration and exploitation of GJO, the algorithm efficiently explores the feature space, selecting relevant features that contribute to accurate forgery classification. This enhance the performance of MSVM by focusing on the most discriminative features. For this work, a dataset named as MICC-F2000 is examined which is managed by 2000 images in this 1300 are original and 700 are forged images. The result shows that the proposed GJO based MSVM model delivers the performance metrics like accuracy, sensitivity, specificity, precision, and MCC values about 99.47 %, 97.01%, 96.51%, 99.62%, and 96.39% respectively which ensures accurate forgery detection compared with existing methods such as SSDAE-GOA-SHO, ConvLSTM, CNN and dual branch CNN methods.

Focus and Shake Algorithm: A New Stochastic Optimization Employing Strict and Randomized Dimension Mappings

“…Remora optimization algorithm (ROA) was combined with the long-short term memory classifier to predict lung cancer using histopathology images [2]. Golden jackal optimization (GJO) has been combined with a support vector machine (SVM) to detect and classify digital image forgery [3].…”