Open Access

ARTICLE

Weber Law Based Approach for Multi-Class Image Forgery Detection

by Arslan Akram^1,3, Javed Rashid^2,3,4, Arfan Jaffar¹, Fahima Hajjej⁵, Waseem Iqbal⁶, Nadeem Sarwar^7,*

1 Department of Computer Science, Superior University, Lahore, 54000, Pakistan
2 Information Technology Services, University of Okara, Okara, 56300, Pakistan
3 Departmet of Computer Science, MLC Lab, Okara, 56300, Pakistan
4 Department of CS&SE, International Islamic University, Islamabad, 44000, Pakistan
5 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, 11671, Saudi Arabia
6 Department of Software Engineering, Superior University, Lahore, 54000, Pakistan
7 Department of Computer Science, Bahria University, Lahore Campus, Lahore, 54600, Pakistan

* Corresponding Author: Nadeem Sarwar. Email:

(This article belongs to the Special Issue: The Next Generation of Artificial Intelligence and the Intelligent Internet of Things)

Computers, Materials & Continua 2024, 78(1), 145-166. https://doi.org/10.32604/cmc.2023.041074

Received 10 April 2023; Accepted 18 July 2023; Issue published 30 January 2024

Abstract

Today’s forensic science introduces a new research area for digital image analysis for multimedia security. So, Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or create misleading publicity by using tempered images. Exiting forgery detection methods can classify only one of the most widely used Copy-Move and splicing forgeries. However, an image can contain one or more types of forgeries. This study has proposed a hybrid method for classifying Copy-Move and splicing images using texture information of images in the spatial domain. Firstly, images are divided into equal blocks to get scale-invariant features. Weber law has been used for getting texture features, and finally, XGBOOST is used to classify both Copy-Move and splicing forgery. The proposed method classified three types of forgeries, i.e., splicing, Copy-Move, and healthy. Benchmarked (CASIA 2.0, MICCF200) and RCMFD datasets are used for training and testing. On average, the proposed method achieved 97.3% accuracy on benchmarked datasets and 98.3% on RCMFD datasets by applying 10-fold cross-validation, which is far better than existing methods.

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Keywords

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