Publication:
Effective Deep Features for Image Splicing Detection

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dc.citedby4
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.accessioned2023-05-29T09:10:14Z
dc.date.available2023-05-29T09:10:14Z
dc.date.issued2021
dc.descriptionCorrelation methods; Deep learning; Image classification; Image enhancement; Alexnet model; Canonical correlation analyse classifier; Canonical correlations analysis; Deep feature; Digital image; Digital image forgery; Image forgery; Image forgery detections; Image splicing; Image splicing detection; Classification (of information)en_US
dc.description.abstractIn the last few years, Digital image forgery (DIF) detection has become a prominent subject. Image splicing is a frequent approach for making digital image forgeries. Image splicing creates forged images that are hard to detect immediately. The detection accuracy of most existing image splicing detection algorithms is low, thus there is room for improvement. Therefore, this research provides an image splicing detection (ISD) method based on deep learning. The proposed image splicing detection has three stages: (1) RGB image conversion and image size fitting are examples of image pre-processing. (2) Using the pre-Trained CNN AlexNet model, we extract the final discriminative feature for a preprocessed image. (3) Finally, the generated feature representation is used to train a Canonical Correlation Analysis (CCA) classifier for binary classification (authentic/forged). The accuracy of the proposed approach using a pre-Trained AlexNet model based deep features with CCA classifier is equal to 98.79 % when evaluated on the CASIA v1.0 splicing image forgery database. In comparison, the proposed surpassed existing methods. In the future, the proposed could be applied to other types of image forgery, such as image retouching. � 2021 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ICSET53708.2021.9612569
dc.identifier.epage193
dc.identifier.scopus2-s2.0-85123353742
dc.identifier.spage189
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85123353742&doi=10.1109%2fICSET53708.2021.9612569&partnerID=40&md5=d3e75920405ad7a5458edbe542fd02de
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/26416
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitle2021 IEEE 11th International Conference on System Engineering and Technology, ICSET 2021 - Proceedings
dc.titleEffective Deep Features for Image Splicing Detectionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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