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Efficiency and Effectiveness Metrics in Evolutionary Algorithms and Their Application Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Image Splicing Detection Based on Markov Features in QDCT Domain

Authors : Ce Li, Qiang Ma, Limei Xiao, Ming Li, Aihua Zhang

Published in: Intelligent Computing Theories and Methodologies

Publisher: Springer International Publishing

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Abstract

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.

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previous chapter A BRMF-Based Model for Missing-Data Estimation of Image Sequence
next chapter Implementation of 3-D RDPAD Algorithm on Follicle Images Based CUDA
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Metadata
Title
Image Splicing Detection Based on Markov Features in QDCT Domain
Authors
Ce Li
Qiang Ma
Limei Xiao
Ming Li
Aihua Zhang
Copyright Year
2015
Book
Intelligent Computing Theories and Methodologies

Print ISBN: 978-3-319-22185-4

Electronic ISBN: 978-3-319-22186-1

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-22186-1_17

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