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Digital image splicing detection based on Markov features in block DWT domain

  • Qingbo Zhang
  • Wei Lu
  • Ruxin Wang
  • Guoqiang Li
Article

Abstract

Image splicing is very common and fundamental in image tampering. Many splicing detection schemes based on Markov features in transform domain have been proposed. Based on previous studies, the traditional DWT based schemes perform not better than the DCT based schemes. In this paper, a block DWT based scheme is proposed to improve the detection performance of the DWT based scheme. Firstly, the block DWT is applied on the source image. Then, the Markov features are constructed in block DWT domain to characterize the dependency among wavelet coefficients across positions. After that, feature selection method SVM-RFE is used to reduce the dimensionality of features. Finally, Support Vector Machine is exploited to classify the authentic and spliced images. Experiment results show that the detection performance of the features extracted in DWT domain can be improved with block DWT based scheme. And then, in order to further clarify the phenomenon about the traditional DWT based schemes perform not better than the DCT based schemes, a detail comparison between the two kinds of schemes is proposed based on a set of experiments. The results show that the DWT based scheme is more applicable and powerful than the DCT based scheme, and the DCT based scheme is more suitable for handling these datasets which generated with the process of JPEG compression.

Keywords

Digital image forensics Image splicing detection Discrete wavelet transform Discrete cosine transform JPEG compression 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. U1736118), the Natural Science Foundation of Guangdong (No. 2016A030313350), the Special Funds for Science and Technology Development of Guangdong (No. 2016KZ010103), the Key Project of Scientific Research Plan of Guangzhou (No. 201804020068), the Fundamental Research Funds for the Central Universities (No. 16lgjc83 and No. 17lgjc45).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Data and Computer Science, Guangdong Key Laboratory of Information Security TechnologySun Yat-sen UniversityGuangzhouChina
  2. 2.State Key Laboratory of Information SecurityInstitute of Information Engineering, Chinese Academy of SciencesBeijingChina
  3. 3.School of SoftwareShanghai Jiao Tong UniversityShanghaiChina

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