Seam carving is an excellent content-aware image resizing technology widely used, and it is also a means of image tampering. Once an image is seam carved, the distribution of magnitude levels for the pixel intensity differences in the local neighborhood will be changed, which can be considered as a clue for detection of seam carving for forensic purposes. In order to accurately describe the distribution of magnitude levels for the pixel intensity differences in the local neighborhood, local neighborhood magnitude occurrence pattern (LNMOP) is proposed in this paper. The LNMOP pattern describes the distribution of intensity difference by counting up the number of magnitude level occurrences in the local neighborhood. Based on this, a forensic approach for image seam carving is proposed in this paper. Firstly, the histogram features of LNMOP and HOG (histogram of oriented gradient) are extracted from the images for seam carving forgery detection. Then, the final features for the classifier are selected from the extracted LNMOP features. The LNMOP feature selection method based on HOG feature hierarchical matching is proposed, which determines the LNMOP features to be selected by the HOG feature level. Finally, support vector machine (SVM) is utilized as a classifier to train and test by the above selected features to distinguish tampered images from normal images. In order to create training sets and test sets, images are extracted from the UCID image database. The experimental results of a large number of test images show that the proposed approach can achieve an overall better performance than the state-of-the-art approaches.
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