Wavelet domain audio steganalysis for multiplicative embedding model

Steganalysis is taken as a countermeasure to steganography and is detecting and decoding hidden data within a given media. There has been quite some effort in audio steganalysis for additive embedding model. However, when they distinguish the cover-audio signal with multiplicative noise and the stego-audio signal, results are disappointing. In this paper, a wavelet domain audio steganalysis method for multiplicative embedding model is proposed. The test audio signal is firstly calculated its absolute value and logarithm. Multiplicative noise is changed to additive noise. Then features are extracted. At last, support vector machine (SVM) is utilized as a classifier to distinguish the cover-audio signal and the stego-audio signal. Simulation results show that the detection rates are greater than 94% and the method is effective.