An algorithm of echo steganalysis based on Bayes classifier

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 second order and third order center moments of the peak frequency as feature vector. The Bayes classifier is utilized in classification. All of the 1200 audio signals are trained and tested in our extensive experiment work. With randomly selected 600 audios for training and remaining 600 audios 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 80%, thus indicating significant advancement in steganalysis.