LSB steganalysis of speech data based on distance measure and ML decision

Steganalysis can be used to classify an object whether or not it contains hidden information. In this article, is presented, a novel approach to detect the presence of least significant bit (LSB) steganographic messages in the voice secure communication system. A distance measure, which has proven to be sensitive to LSB steganography by analysis of variance (ANOVA), is denoted to estimate the difference between the host signal and the stego signal. Then an maximum likelihood (ML) decision is combined to form the classifier. Statistical experiments show that the proposed approach has a highly accurate rate and low computational complexity.