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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

Ping Zhong, Mengdi Li, Kai Mu, Juan Wen, Yiming Xue
Copyright: © 2019 |Volume: 11 |Issue: 1 |Pages: 12
ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781522565147|DOI: 10.4018/IJDCF.2019010106
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MLA

Zhong, Ping, et al. "Image Steganalysis in High-Dimensional Feature Spaces with Proximal Support Vector Machine." IJDCF vol.11, no.1 2019: pp.78-89. http://doi.org/10.4018/IJDCF.2019010106

APA

Zhong, P., Li, M., Mu, K., Wen, J., & Xue, Y. (2019). Image Steganalysis in High-Dimensional Feature Spaces with Proximal Support Vector Machine. International Journal of Digital Crime and Forensics (IJDCF), 11(1), 78-89. http://doi.org/10.4018/IJDCF.2019010106

Chicago

Zhong, Ping, et al. "Image Steganalysis in High-Dimensional Feature Spaces with Proximal Support Vector Machine," International Journal of Digital Crime and Forensics (IJDCF) 11, no.1: 78-89. http://doi.org/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.