High-SNR steganography for digital audio signal in the wavelet domain

Imperceptible, robust, and embedding capacity are three main requirements for the steganography of digital audio signal. To enhance them, this study presents a novel steganography for digital audio signal in the wavelet domain. Since the performance of imperceptible and robust are usually in term of signal-to-noise ratio (SNR) and bit-error-rate (BER), we propose a quantization-based optimization model to maximize SNR and reduce BER in embedding secret message. In the proposed model, quantization technique with unknow coefficients of discrete wavelet transform (DWT) is rewritten as the first constraint. The adjustment of scaling DWT coefficients is considered as the second constraint. At the same time, signal-to-noise ratio (SNR) is converted into a performance index. In solving this model, we use matrix operations and Lagrange multiplier to obtain optimal DWT coefficients and scaling factors. Moreover, the invariant feature of the scaling factors against amplitude scaling attack is proved. In extraction, secret message can be detected without original audio signal. Experimental results show that the proposed steganography has high SNR and strong robustness against many malicious attacks when comparing to some exiting methods.