Time domain speech steganalysis method based on multiplicative embedding model
Liang Ye, Xiu-Juan Qi, Lin-Jie Lv, Yi Zuo · 2012
Steganalysis is detecting and decoding hidden data within a given media and is taken as a countermeasure to steganography. There has been quite some effort in audio steganalysis for additive embedding model. But, results are disappointing when they distinguish the cover-audio signal with multiplicative noise and the stego-audio signal. In this paper, multiplicative noise is changed to additive noise. A time domain audio steganalysis method for multiplicative embedding model is proposed. The test audio signal is calculated its absolute value and logarithm at first. Then features are extracted. Then, 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 89%. The method is effective.