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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., Comment: 7 pages, 4 figures, 3 tables, 2016 1st International Conference on New Research Achievements in Electrical and Computer Engineering, https://en.civilica.com/Paper-CBCONF01-CBCONF01_0107=Calibrated-Audio-Steganalysis.html

Recently, merging signal processing techniques with information security services has found a lot of attention. Steganography and steganalysis are among those trends. Like their counterparts in cryptology, steganography and steganalysis are in a constant battle. Steganography methods try to hide the presence of covert messages in innocuous-looking data, whereas steganalysis methods try to reveal existence of such messages and to break steganography methods. The stream nature of audio signals, their popularity, and their wide spread usage make them very suitable media for steganography. This has led to a very rich literature on both steganography and steganalysis of audio signals. This paper intends to conduct a comprehensive review of audio steganalysis methods aggregated over near fifteen years. Furthermore, we implement some of the most recent audio steganalysis methods and conduct a comparative analysis on their performances. Finally, the paper provides some possible directions for future researches on audio steganalysis., Comment: Submitted journal paper