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

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. (Published)

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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.

Item Type: Article
Additional Information: Indexed by Scopus
Uncontrolled Keywords: Image forgery; Image splicing; Fractional entropy; Fractional calculus; Discrete wavelet transform
Subjects: Q Science > QA Mathematics
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Faculty/Division: Faculty of Computer System And Software Engineering
Depositing User: Mrs Norsaini Abdul Samat
Date Deposited: 22 Nov 2019 03:06
Last Modified: 22 Nov 2019 03:06
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