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Digital Image Splicing Detection Based on Markov Features in QDCT and QWT Domain

Digital Image Splicing Detection Based on Markov Features in QDCT and QWT Domain
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Author(s): Ruxin Wang (School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China), Wei Lu (School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China), Jixian Li (School of Data and Computer Science, Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China), Shijun Xiang (College of Information Science and Technology, Jinan University, Guangzhou, China), Xianfeng Zhao (The State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China)and Jinwei Wang (School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China)
Copyright: 2020
Pages: 19
Source title: Digital Forensics and Forensic Investigations: Breakthroughs in Research and Practice
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-3025-2.ch006

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Abstract

Image splicing detection is of fundamental importance in digital forensics and therefore has attracted increasing attention recently. In this article, a color image splicing detection approach is proposed based on Markov transition probability of quaternion component separation in quaternion discrete cosine transform (QDCT) domain and quaternion wavelet transform (QWT) domain. First, Markov features of the intra-block and inter-block between block QDCT coefficients are obtained from the real parts and three imaginary parts of QDCT coefficients, respectively. Then, additional Markov features are extracted from the luminance (Y) channel in the quaternion wavelet transform domain to characterize the dependency of position among quaternion wavelet sub-band coefficients. Finally, an ensemble classifier (EC) is exploited to classify the spliced and authentic color images. The experiment results demonstrate that the proposed approach can outperform some state-of-the-art methods.

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