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.
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