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Effective Deep Features for Image Splicing Detection

Correlation 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...

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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-264162023-05-29T17:10:14Z Effective Deep Features for Image Splicing Detection Ahmed I.T. Hammad B.T. Jamil N. 57193324906 57193327622 36682671900 Correlation 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) In 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. Final 2023-05-29T09:10:14Z 2023-05-29T09:10:14Z 2021 Conference Paper 10.1109/ICSET53708.2021.9612569 2-s2.0-85123353742 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85123353742&doi=10.1109%2fICSET53708.2021.9612569&partnerID=40&md5=d3e75920405ad7a5458edbe542fd02de https://irepository.uniten.edu.my/handle/123456789/26416 189 193 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 Correlation 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)
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.
Effective Deep Features for Image Splicing Detection
author_sort Ahmed I.T.
title Effective Deep Features for Image Splicing Detection
title_short Effective Deep Features for Image Splicing Detection
title_full Effective Deep Features for Image Splicing Detection
title_fullStr Effective Deep Features for Image Splicing Detection
title_full_unstemmed Effective Deep Features for Image Splicing Detection
title_sort effective deep features for image splicing detection
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806424395554488320
score 13.223943

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