Weber Law Based Approach for Multi-Class Image Forgery Detection  

作　　者：Arslan Akram Javed Rashid Arfan Jaffar Fahima Hajjej Waseem Iqbal Nadeem Sarwar 

机构地区：[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

出　　处：《Computers, Materials & Continua》2024年第1期145-166,共22页计算机、材料和连续体（英文）

基　　金：funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2024R236),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.

摘　　要：Today’s forensic science introduces a new research area for digital image analysis formultimedia security.So,Image authentication issues have been raised due to the wide use of image manipulation software to obtain an illegitimate benefit or createmisleading publicity by using tempered images.Exiting forgery detectionmethods 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 hybridmethod 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.

关 键 词：Copy-Move and splicing non-overlapping block division texture features weber law spatial domain xgboost 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]