A Machine Learning Method for Detecting the Trace of Seam Carving

Image retargeting is a manipulation approach for resizing the images while aiming to keep the image distortion at a low level. Detecting image retargeting is of importance in image forensics or sometimes of importance in checking the originality. The aim of this paper is to introduce a new blind det...

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Autores principales:	Zehra Karapinar Senturk, Devrim Akgun
Formato:	article
Lenguaje:	EN
Publicado:	Kaunas University of Technology 2021
Materias:	image retargeting seam carving machine learning. support vector machine local binary patterns image forensics Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/11c3ec59970a46c0a6d287cc4f02a959
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id	oai:doaj.org-article:11c3ec59970a46c0a6d287cc4f02a959
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spelling	oai:doaj.org-article:11c3ec59970a46c0a6d287cc4f02a9592021-11-04T14:14:15ZA Machine Learning Method for Detecting the Trace of Seam Carving1392-12152029-573110.5755/j02.eie.29050https://doaj.org/article/11c3ec59970a46c0a6d287cc4f02a9592021-10-01T00:00:00Zhttps://eejournal.ktu.lt/index.php/elt/article/view/29050https://doaj.org/toc/1392-1215https://doaj.org/toc/2029-5731Image retargeting is a manipulation approach for resizing the images while aiming to keep the image distortion at a low level. Detecting image retargeting is of importance in image forensics or sometimes of importance in checking the originality. The aim of this paper is to introduce a new blind detection method for identifying retargeted images based on seam carving. For this purpose, a new method based on stripes at various numbers, Local Binary Pattern (LBP) transform, and energy map is introduced. The sub-images were obtained from square root of the energy map of LBP transform in the form of stripes for the feature extraction and these were evaluated in terms of several statistical features. The features extracted both from the natural and the seam carved images were used to train a Support Vector Machine (SVM) as a binary classifier. Experimental results were obtained using four-fold cross validation to improve the validity of the results during the evaluation process. According to the experiments, the proposed method produces improved accuracies when compared with the state-of-the-art solutions for the image retargeting detection based on seam carving.Zehra Karapinar SenturkDevrim AkgunKaunas University of Technologyarticleimage retargetingseam carvingmachine learning.support vector machinelocal binary patternsimage forensicsElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENElektronika ir Elektrotechnika, Vol 27, Iss 5, Pp 59-66 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	image retargeting seam carving machine learning. support vector machine local binary patterns image forensics Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	image retargeting seam carving machine learning. support vector machine local binary patterns image forensics Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zehra Karapinar Senturk Devrim Akgun A Machine Learning Method for Detecting the Trace of Seam Carving
description	Image retargeting is a manipulation approach for resizing the images while aiming to keep the image distortion at a low level. Detecting image retargeting is of importance in image forensics or sometimes of importance in checking the originality. The aim of this paper is to introduce a new blind detection method for identifying retargeted images based on seam carving. For this purpose, a new method based on stripes at various numbers, Local Binary Pattern (LBP) transform, and energy map is introduced. The sub-images were obtained from square root of the energy map of LBP transform in the form of stripes for the feature extraction and these were evaluated in terms of several statistical features. The features extracted both from the natural and the seam carved images were used to train a Support Vector Machine (SVM) as a binary classifier. Experimental results were obtained using four-fold cross validation to improve the validity of the results during the evaluation process. According to the experiments, the proposed method produces improved accuracies when compared with the state-of-the-art solutions for the image retargeting detection based on seam carving.
format	article
author	Zehra Karapinar Senturk Devrim Akgun
author_facet	Zehra Karapinar Senturk Devrim Akgun
author_sort	Zehra Karapinar Senturk
title	A Machine Learning Method for Detecting the Trace of Seam Carving
title_short	A Machine Learning Method for Detecting the Trace of Seam Carving
title_full	A Machine Learning Method for Detecting the Trace of Seam Carving
title_fullStr	A Machine Learning Method for Detecting the Trace of Seam Carving
title_full_unstemmed	A Machine Learning Method for Detecting the Trace of Seam Carving
title_sort	machine learning method for detecting the trace of seam carving
publisher	Kaunas University of Technology
publishDate	2021
url	https://doaj.org/article/11c3ec59970a46c0a6d287cc4f02a959
work_keys_str_mv	AT zehrakarapinarsenturk amachinelearningmethodfordetectingthetraceofseamcarving AT devrimakgun amachinelearningmethodfordetectingthetraceofseamcarving AT zehrakarapinarsenturk machinelearningmethodfordetectingthetraceofseamcarving AT devrimakgun machinelearningmethodfordetectingthetraceofseamcarving
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A Machine Learning Method for Detecting the Trace of Seam Carving

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