A Machine Learning Method for Detecting the Trace of Seam Carving

Authors

  • Zehra Karapinar Senturk Department of Computer Engineering, Engineering Faculty, Duzce University, Turkey
  • Devrim Akgun Software Engineering Department, Computer and Information Science Faculty, Sakarya University, Turkey

DOI:

https://doi.org/10.5755/j02.eie.29050

Keywords:

Image retargeting, Seam carving, Machine learning., Support Vector Machine, Local binary patterns, Image forensics

Abstract

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.

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Published

2021-09-08

How to Cite

Karapinar Senturk, Z., & Akgun, D. (2021). A Machine Learning Method for Detecting the Trace of Seam Carving. Elektronika Ir Elektrotechnika, 1(1). https://doi.org/10.5755/j02.eie.29050

Issue

Section

SYSTEM ENGINEERING, COMPUTER TECHNOLOGY