Intelligent audio watermarking algorithm using Multi-objective Particle Swarm Optimization
Mustapha Hemis, Bachir Boudraa, Thouraya Merazi-Meksen · 2015
In this paper, we propose an intelligent audio watermarking approach based on QR factorization in wavelet domain by exploiting Multi-objective Particle Swarm Optimization (MOPSO). First, the audio signal is decomposed into low and high frequency ranges by using Discrete Wavelet Transform (DWT). The low frequency part is then segmented into 2-D blocks and QR factorization is applied to each one. The watermark bits are embedded by quantizing the coefficients of resulting R matrices. To achieve best compromise between the conflicting objectives, namely imperceptibility and robustness, the quantization step is optimized using MOPSO process. The watermark can be blindly extracted without the knowledge of the original audio signal. Experimental results show that the proposed method not only maintains high quality of the audio signal, but also is robust against common signal processing manipulations. These results confirm the effectiveness of this method for secure applications such as copyright protection.