Tehnički vjesnik, Vol. 24 No. 6, 2017.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20160804121351
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.org/0000-0002-0770-599X
; Sakarya University, Faculty of Computer and Information Sciences, Computer Engineering Department, Esentepe Campus, 54187, Sakarya, Turkey
Sažetak
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
URI
Datum izdavanja:
3.12.2017.
Posjeta: 2.386 *