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Image Splicing Detection Based on Markov Features in QDCT Domain

机译：QDCT域中基于马尔可夫特征的图像拼接检测

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摘要

Image splicing is very common and fundamental in image tampering. Therefore, image splicing detection has attracted more and more attention recently in digital forensics. Gray images are used directly, or color images are converted to gray images before processing in previous image splicing detection algorithms. However, most natural images are color images. In order to make use of the color information in images, a classification algorithm is put forward which can use color images directly. In this paper, an algorithm based on Markov in Quaternion discrete cosine transform (QDCT) domain is proposed for image splicing detection. The support vector machine (SVM) is exploited to classify the authentic and spliced images. The experiment results demonstrate that the proposed algorithm not only make use of color information of images, but also can achieve high classification accuracy.
机译：图像拼接在图像篡改中非常普遍和基本。因此，图像拼接检测近来在数字取证中越来越受到关注。在以前的图像拼接检测算法中进行处理之前，直接使用灰度图像，或将彩色图像转换为灰度图像。但是，大多数自然图像是彩色图像。为了利用图像中的色彩信息，提出了一种可以直接使用彩色图像的分类算法。提出了一种基于马尔可夫四元数离散余弦变换（QDCT）域的图像拼接检测算法。利用支持向量机（SVM）对真实图像和拼接图像进行分类。实验结果表明，该算法不仅可以利用图像的颜色信息，而且可以达到较高的分类精度。

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