TY  - EJOU
AU  - Akram, Arslan 
AU  - Rashid, Javed 
AU  - Jaffar, Arfan 
AU  - Hajjej, Fahima 
AU  - Iqbal, Waseem 
AU  - Sarwar, Nadeem 

TI  - Weber Law Based Approach for Multi-Class Image Forgery Detection
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 78
IS  - 1
SN  - 1546-2226

AB  - 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.
KW  - Copy-Move and splicing; non-overlapping block division; texture features; weber law; spatial domain; xgboost

DO  - 10.32604/cmc.2023.041074