Publication:
Image Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domain

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dc.citedby10
dc.contributor.authorAhmed I.T.en_US
dc.contributor.authorHammad B.T.en_US
dc.contributor.authorJamil N.en_US
dc.contributor.authorid57193324906en_US
dc.contributor.authorid57193327622en_US
dc.contributor.authorid36682671900en_US
dc.date.accessioned2023-05-29T09:08:49Z
dc.date.available2023-05-29T09:08:49Z
dc.date.issued2021
dc.descriptionFeature extraction; Signal detection; Support vector machines; Copy-move forgeries; Copy-move forgery detections; Detection accuracy; Image preprocessing; Image processing tools; Mean and standard deviations; Spatial features; State-of-the-art approach; Image processingen_US
dc.description.abstractCurrently, digital image forgery (DIF) become more active due to the advent of powerful image processing tools. On a daily, many images are exchanged through the internet, which makes them susceptible to such effects. One of the most popular of the passive image forgery techniques is copy-move forgery. In the Copy-move forgery, the basic process is copy/paste from one area to another in the same image. In this paper, the proposed image copy-move forgery detection (IC-MFDs) involves five stages: image preprocessing, dividing the image into overlapping blocks, calculating the mean and standard deviation of each block, feature vectors are then sorted lexicographically, then feeding the feature vector to the Support Vector Machine (SVM) classifier to identify the image as authentic or forged. Experiments are performed on a standard dataset of copy move forged images MICC-F220 to evaluate the proposed technique. The findings indicate that the proposed IC-MFDs can be extremely accurate in terms of Detection Accuracy (98.44). We also compare some state-of-the-art approaches with our proposed IC-MFDs. It's noted that the findings obtained are better than these approaches. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo9377272
dc.identifier.doi10.1109/CSPA52141.2021.9377272
dc.identifier.epage96
dc.identifier.scopus2-s2.0-85103693083
dc.identifier.spage92
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85103693083&doi=10.1109%2fCSPA52141.2021.9377272&partnerID=40&md5=9f756ff423057e8fcf9488314d97a78c
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26295
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceeding - 2021 IEEE 17th International Colloquium on Signal Processing and Its Applications, CSPA 2021
dc.titleImage Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domainen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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