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.

中文翻译：

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使用新型图案检测图像接缝雕刻。

接缝雕刻是一种广泛使用的出色的内容感知图像调整大小技术，它也是图像篡改的一种手段。一旦对图像进行了接缝雕刻，就将改变局部邻域中像素强度差异的幅度级别分布，这可以被认为是出于法医目的检测接缝雕刻的线索。为了准确描述局部邻域像素强度差异的幅度水平分布，提出了局部邻域幅度发生模式（LNMOP）。LNMOP模式通过计算局部邻域中出现的幅度级别的数量来描述强度差的分布。在此基础上，提出了图像接缝雕刻的法医方法。首先，从图像中提取出LNMOP和HOG的直方图特征（定向梯度直方图）以进行接缝雕刻伪造检测。然后，从提取的LNMOP特征中选择分类器的最终特征。提出了一种基于HOG特征层次匹配的LNMOP特征选择方法，该方法确定了HOG特征层次选择的LNMOP特征。最后，将支持向量机（SVM）用作分类器，通过上述选定特征进行训练和测试，以将篡改图像与正常图像区分开。为了创建训练集和测试集，从UCID图像数据库中提取图像。大量测试图像的实验结果表明，与最先进的方法相比，该方法可以实现总体上更好的性能。