  Calibration is a common practice in image steganalysis for extracting
prominent features. Based on the idea of reembedding, a new set of calibrated
features for audio steganalysis applications are proposed. These features are
extracted from a model that has maximum deviation from human auditory system
and had been specifically designed for audio steganalysis. Ability of the
proposed system is tested extensively. Simulations demonstrate that the
proposed method can accurately detect the presence of hidden messages even in
very low embedding rates. Proposed method achieves an accuracy of 99.3%
(StegHide@0.76% BPB) which is 9.5% higher than the previous R-MFCC based
steganalysis method.

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