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Tehnički vjesnik, Vol.24 No.6 Prosinac 2017.

Izvorni znanstveni članak

Seam carving based image resizing detection using hybrid features

Zehra Karapinar Senturk ; Duzce University, Faculty of Engineering, Department of Computer Engineering, Konuralp Campus, 81620, Duzce, Turkey
Devrim Akgun   ORCID icon ; Sakarya University, Faculty of Computer and Information Sciences, Computer Engineering Department, Esentepe Campus, 54187, Sakarya, Turkey

Puni tekst: engleski, pdf (1 MB) str. 1825-1832 preuzimanja: 61* citiraj
Karapinar Senturk, Z., Akgun, D. (2017). Seam carving based image resizing detection using hybrid features. Tehnički vjesnik, 24(6). doi:10.17559/TV-20160804121351
Puni tekst: hrvatski, pdf (1 MB) str. 1825-1832 preuzimanja: 26* citiraj
Karapinar Senturk, Z., Akgun, D. (2017). Otkrivanje promjene veličine slike temeljeno na tekućem skaliranju uporabom hibridnih značajki. Tehnički vjesnik, 24(6). doi:10.17559/TV-20160804121351

Detection of seam carving-based digital image resizing is a challenging task in image processing field since the method investigates the images on hand semantically. Resizing with seam carving is realized by inserting or removing relatively unimportant pixel paths to/from the images and so the changes in image content are mostly unnoticeable. Local Binary Patterns (LBP), a visual descriptor, unearths local changes in image texture. Therefore, using LBP transform of the images besides intensity values contributes to the detection ratio. In this paper, we proposed a hybrid detection mechanism for more accurate seam carving detection especially in low scaling ratios. We extracted LBP-based and non-LBP based features and trained a Support Vector Machine (SVM) with sixty features. We achieved approximately 9 % improvement in low detection ratios. The experimental results show that more satisfactory detection ratios can be obtained by the proposed hybrid approach.

Ključne riječi
forgery detection; Local Binary Patterns; seam carving; Support Vector Machines

Hrčak ID: 190180



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