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

Feature 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 processing

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Bibliographic Details
Main Authors: Ahmed I.T., Hammad B.T., Jamil N.
Other Authors: 57193324906
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-262952023-05-29T17:08:49Z Image Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domain Ahmed I.T. Hammad B.T. Jamil N. 57193324906 57193327622 36682671900 Feature 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 processing Currently, 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. Final 2023-05-29T09:08:49Z 2023-05-29T09:08:49Z 2021 Conference Paper 10.1109/CSPA52141.2021.9377272 2-s2.0-85103693083 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85103693083&doi=10.1109%2fCSPA52141.2021.9377272&partnerID=40&md5=9f756ff423057e8fcf9488314d97a78c https://irepository.uniten.edu.my/handle/123456789/26295 9377272 92 96 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Feature 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 processing
author2 57193324906
author_facet 57193324906
Ahmed I.T.
Hammad B.T.
Jamil N.
format Conference Paper
author Ahmed I.T.
Hammad B.T.
Jamil N.
spellingShingle Ahmed I.T.
Hammad B.T.
Jamil N.
Image Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domain
author_sort Ahmed I.T.
title Image Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domain
title_short Image Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domain
title_full Image Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domain
title_fullStr Image Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domain
title_full_unstemmed Image Copy-Move Forgery Detection Algorithms Based on Spatial Feature Domain
title_sort image copy-move forgery detection algorithms based on spatial feature domain
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806426279233191936
score 13.214268

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