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Image Steganalysis in High-Dimensional Feature Spaces with Proximal Support Vector Machine

Image Steganalysis in High-Dimensional Feature Spaces with Proximal Support Vector Machine
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Author(s): Ping Zhong (College of Science, China Agricultural University, Beijing, China), Mengdi Li (College of Information and Electrical Engineering, China Agricultural University, Beijing, China), Kai Mu (College of Information and Electrical Engineering, China Agricultural University, Beijing, China), Juan Wen (College of Information and Electrical Engineering, China Agricultural University, Beijing, China) and Yiming Xue (College of Information and Electrical Engineering, China Agricultural University, Beijing, China)
Copyright: 2019
Volume: 11
Issue: 1
Pages: 12
Source title: International Journal of Digital Crime and Forensics (IJDCF)
Editor(s)-in-Chief: Feng Liu (Chinese Academy of Sciences, China)
DOI: 10.4018/IJDCF.2019010106

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Abstract

This article presents the linear Proximal Support Vector Machine (PSVM) to the image steganalysis, and further generates a very efficient method called PSVM-LSMR through implementing PSVM by the state-of-the-art optimization method Least Square Minimum-Residual (LSMR). Also, motivated by extreme learning machine (ELM), a nonlinear algorithm PSVM-ELM is proposed for the image steganalysis. It is shown by the experiments with the wide stego schemes and rich steganalysis feature sets in both the spatial and JPEG domains that the PSVM can achieve comparable performance with Fisher Linear Discriminant (FLD) and ridge regression, and its computational time is far more less than that of them on large feature sets. The PSVM-LSMR is comparable to Ridge Regression implemented by LSMR (RR-LSMR), and both of them require the least computational time among all the competitions when dealing with medium or large feature sets. The nonlinear PSVM-ELM performs comparably or even better than FLD and ridge regression for the spatial domain steganographic schemes, and its computational time is apparently less than that of them on large feature sets.

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