Weber Law Based Approach for Multi-Class Image Forgery Detection

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. E...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computers, materials & continua Ročník 78; číslo 1; s. 145 - 166
Hlavní autori: Akram, Arslan, Rashid, Javed, Jaffar, Arfan, Hajjej, Fahima, Iqbal, Waseem, Sarwar, Nadeem
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Henderson Tech Science Press 2024
Predmet:
Classification
Datasets
Digital imaging
Forgery
Image analysis
Image manipulation
Texture
ISSN:1546-2226, 1546-2218, 1546-2226
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2023.041074