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A quantitative steganalyzer is an estimator of the number of embedding changes introduced by a specific embedding operation. Since for most algorithms the number of embedding changes correlates with the message length, quantitative steganalyzers are important forensic tools. In this paper, a general method for constructing quantitative steganalyzers from features used in blind detectors is proposed. The core of the method is support vector regression, which is used to learn the mapping betweendoi:10.1109/tifs.2011.2175918 fatcat:d2n4ehju6zcujgd3jlvqkq4r6q