Performance improvement of spread spectrum additive data hiding over codec-distorted voice channels

This paper considers the problem of covert communication through dedicated voice channels by embedding secure data in the cover speech signal utilizing spread spectrum additive data hiding. The cover speech signal is modeled by a Generalized Gaussian (GGD) random variable and the Maximum A Posteriori (MAP) detector for extraction of the covert message is designed and its reliable performance is verified both analytically and by simulations. The idea of adaptive estimation of detector parameters is proposed to improve detector performance and overcome voice non-stationarity. The detector's bit error rate (BER) is investigated for both blind and semi-blind cases in which the GGD shape parameter needed for optimum detection is either estimated from the stego or cover signal respectively. The simulation results also show that the proposed method achieves acceptable robustness against the lossy compression attack by different compression rates of Adaptive Multi Rate (AMR) voice codec.