• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

New Blind Steganalysis Framework Combining Image Retrieval and Outlier Detection

Vol. 10, No.12, December 31, 2016
10.3837/tiis.2016.12.026, Download Paper (Free):

Abstract

The detection accuracy of steganalysis depends on many factors, including the embedding algorithm, the payload size, the steganalysis feature space and the properties of the cover source. In practice, the cover source mismatch (CSM) problem has been recognized as the single most important factor negatively affecting the performance. To address this problem, we propose a new framework for blind, universal steganalysis which uses traditional steganalyst features. Firstly, cover images with the same statistical properties are searched from a reference image database as aided samples. The test image and its aided samples form a whole test set. Then, by assuming that most of the aided samples are innocent, we conduct outlier detection on the test set to judge the test image as cover or stego. In this way, the framework has removed the need for training. Hence, it does not suffer from cover source mismatch. Because it performs anomaly detection rather than classification, this method is totally unsupervised. The results in our study show that this framework works superior than one-class support vector machine and the outlier detector without considering the image retrieval process.


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Cite this article

[IEEE Style]
Yunda Wu, Tao Zhang, Xiaodan Hou and Chen Xu, "New Blind Steganalysis Framework Combining Image Retrieval and Outlier Detection," KSII Transactions on Internet and Information Systems, vol. 10, no. 12, pp. 6206-6219, 2016. DOI: 10.3837/tiis.2016.12.026

[ACM Style]
Wu, Y., Zhang, T., Hou, X., and Xu, C. 2016. New Blind Steganalysis Framework Combining Image Retrieval and Outlier Detection. KSII Transactions on Internet and Information Systems, 10, 12, (2016), 6206-6219. DOI: 10.3837/tiis.2016.12.026