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• W3089763054 startingPage "106728" @default.
• W3089763054 abstract "Image manipulation plays important role in fake news spreading and it may cause ethical, economic, or political problems for people and sometimes for countries. Image integrity verification becomes a very important research issue due to increasing the forged images on the Internet and social media. The objective of this paper is presenting an accurate approach for digital image forgery detection has enough capability to sense any small image tampering and robustness against image manipulation attacks. The first step in the proposed approach is converting the RGB image into YCbCr space, then, the Hilbert–Huang Transform (HHT) features extracted from the chrominance-red component Cr, then, three different classifiers; Support Vector Machines (SVM), K-Nearest Neighbors (KNN), and Artificial Neuron Networks (ANN) have been tested and compared for image classification into authentic or forged. The results are verified using Structural-Similarity (SSIM) to calculate the forgery detection accuracy. The proposed approach has been tested with seven different manipulation images datasets; CASIA-V1, CASIA-V2, MICC-F2000, MICC-F600, MICC-F220, CoMoFoD and additional dataset collected from different Internet websites and social media. Furthermore, the proposed approach has been tested against post-processing attacks such as; image compression, adding Gaussian noises or adjusting the contrast of the image. The results show that, SVM classifier has achieved the highest accuracy compared to ANN and KNN classifiers. The proposed approach has been compared with other published approaches, and the comparison proved its superiority over the previously published approaches." @default.
• W3089763054 created "2020-10-08" @default.
• W3089763054 creator A5029421514 @default.
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• W3089763054 date "2020-12-01" @default.
• W3089763054 modified "2023-09-23" @default.
• W3089763054 title "An efficient approach for forgery detection in digital images using Hilbert–Huang​ transform" @default.
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• W3089763054 doi "https://doi.org/10.1016/j.asoc.2020.106728" @default.
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