An Autoregressive Time Delay Neural Network for speech steganalysis

Hiding a secret message in speech signal, called steganography, is used to provide secure communication. The detection of hidden information in the transmitted message called steganalysis. The purpose of steganalysis is to identify the presence of embedded information, and does not actually attempt to extract or decode the hidden data. An automated method is required for detecting the existence of hidden message, since the huge amount of channeled information. However, the development and evaluation of steganalysis algorithms is a challenging task. In this paper we advocate a new steganalysis technique to classify a speech as having hidden information or not, using a powerful and sophisticated classifier called Autoregressive Time Delay Neural Network (AR-TDNN). The originality of this AR-TDNN is its quite ability to detect secret messages hidden with different steganographic algorithms, although the variation of detection rate depends on the particular hiding techniques and amount of hidden information.