Syndrome code data hiding using statistical modeling with Markov chains

Some fields of an encoded speech or audio signal´s bit stream, which varies according to encoder´s type, can be modeled by Markov chains. In this paper, a novel “syndrome code data hiding using Markov chains” is proposed where secret data is embedded into the syndromes of the C(N, K) linear block codes as in matrix embedding. However, the proposed method randomly chooses a codeword from 2^K possible code words according to the Markov chain´s transition probabilities, which is different from matrix embedding method. Performance of the proposed method is compared with that of least significant bit and matrix embedding methods employed on GSM 6.10 coder. The simulation results show that data hiding using statistical modeling with Markov chains preserves the original bit stream´s entropy, leading to undetectability in terms of steganalysis. Unfortunately secret data embedding efficiency is decreased.