An algorithm of echo steganalysis based on power cepstrum and pattern classification

Audio steganalysis has attracted more attentions recently. Echo steganalysis is one of the most challenging research fields. In this paper, an effective steganalysis method based on statistical moments of peak frequency is proposed. Combined with power cepstrum, it statistically analyzes the peak frequency using short window extracting, and then calculates the eight high order center moments of peak frequency as feature vector. The SVM classifier is utilized in classification. All of the 1200 audio signals are trained and tested in out extensive experiment work. With randomly selected 600 audio signals for training and remaining 600 audio signals for testing, and with various embedding parameters combinations such as hiding segment length, attenuation coefficient, echo delay for hiding, the proposed steganalysis algorithm can steadily achieve a correct classification rate of 85%. Experimental results and theoretical verification show that this method is an effective method of audio echo steganalysis.