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A Universal Image Steganalysis System Based On Double Sparse Representation Classification (DSRC)
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DOI：10.1007/s11042-017-5201-0.png)
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Abstract
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Achieving high rates of detection in low rates of embedding is still a challenging problem in many steganalysis systems. The newly proposed steganalysis system based on sparse representation classifier has shown remarkable detection rates in low embedding rate. In this paper, we propose a new steganalysis system based on double sparse representation classifier. We compare our proposed method with other steganalysis systems which use different classifier (including nearest neighbor, support vector machine, ensemble support vector machine and sparse representation). In all of our experiments, input features to the classifier are fixed and the ability of classifier is examined. Also we provide a complexity analysis in terms of execution time for different classifier. In most of experiments, our proposed method shows superior performance in terms of detection rate and complexity for low embedding rates.
Keywords:
Steganography
Steganalysis
Dictionary learning
Double sparse representation
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