$New texture descriptor based on modified fractional entropy for digital image splicing forgery detection$

New texture descriptor based on modified fractional entropy for digital image splicing forgery detection

Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more i...

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Main Authors:	Hamid, A. Jalab, Subramaniam, Thamarai, Rabha, W. Ibrahim, Kahtan, Hasan, Nurul F., Mohd Noor
Format:	Article
Language:	English
Published:	MDPI AG 2019
Subjects:	QA Mathematics TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/25710/ http://umpir.ump.edu.my/id/eprint/25710/ http://umpir.ump.edu.my/id/eprint/25710/ http://umpir.ump.edu.my/id/eprint/25710/1/New%20texture%20descriptor%20based%20on%20modified%20fractional%20entropy.pdf

id	ump-25710
recordtype	eprints
spelling	ump-257102019-11-22T03:06:46Z http://umpir.ump.edu.my/id/eprint/25710/ New texture descriptor based on modified fractional entropy for digital image splicing forgery detection Hamid, A. Jalab Subramaniam, Thamarai Rabha, W. Ibrahim Kahtan, Hasan Nurul F., Mohd Noor QA Mathematics TK Electrical engineering. Electronics Nuclear engineering Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors. MDPI AG 2019-04-01 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/25710/1/New%20texture%20descriptor%20based%20on%20modified%20fractional%20entropy.pdf Hamid, A. Jalab and Subramaniam, Thamarai and Rabha, W. Ibrahim and Kahtan, Hasan and Nurul F., Mohd Noor (2019) New texture descriptor based on modified fractional entropy for digital image splicing forgery detection. Entropy, 21 (4). pp. 1-9. ISSN 1099-4300 https://doi.org/10.3390/e21040371 https://doi.org/10.3390/e21040371
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	QA Mathematics TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA Mathematics TK Electrical engineering. Electronics Nuclear engineering Hamid, A. Jalab Subramaniam, Thamarai Rabha, W. Ibrahim Kahtan, Hasan Nurul F., Mohd Noor New texture descriptor based on modified fractional entropy for digital image splicing forgery detection
description	Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors.
format	Article
author	Hamid, A. Jalab Subramaniam, Thamarai Rabha, W. Ibrahim Kahtan, Hasan Nurul F., Mohd Noor
author_facet	Hamid, A. Jalab Subramaniam, Thamarai Rabha, W. Ibrahim Kahtan, Hasan Nurul F., Mohd Noor
author_sort	Hamid, A. Jalab
title	New texture descriptor based on modified fractional entropy for digital image splicing forgery detection
title_short	New texture descriptor based on modified fractional entropy for digital image splicing forgery detection
title_full	New texture descriptor based on modified fractional entropy for digital image splicing forgery detection
title_fullStr	New texture descriptor based on modified fractional entropy for digital image splicing forgery detection
title_full_unstemmed	New texture descriptor based on modified fractional entropy for digital image splicing forgery detection
title_sort	new texture descriptor based on modified fractional entropy for digital image splicing forgery detection
publisher	MDPI AG
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25710/ http://umpir.ump.edu.my/id/eprint/25710/ http://umpir.ump.edu.my/id/eprint/25710/ http://umpir.ump.edu.my/id/eprint/25710/1/New%20texture%20descriptor%20based%20on%20modified%20fractional%20entropy.pdf
first_indexed	2023-09-18T22:39:38Z
last_indexed	2023-09-18T22:39:38Z
_version_	1777416828359802880

New texture descriptor based on modified fractional entropy for digital image splicing forgery detection

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