Self-synchronization blind audio watermarking based on (t, n) threshold

A self-synchronization blind audio watermarking based on (t, n) threshold in wavelet transform is proposed. This technique can extract the watermark without using the original watermark and the host audio during the detection. It is a completely blind method during watermark detection. Watermark was divided into n shadows signal according to secret sharing scheme before the embedding process, and Shamir's (t, n) threshold scheme was used to distribute the watermark shadows to the users. Lagrange interpolation polynomial was used to realize it. Results show that t or more of those signal shadows can reconstruct the secret watermark, while t-1 or less signal shadows could not do it. The watermark is meaningful speech and it was produced independent of the original audio. The watermark embedded intensity depended on the masking effect of human audio system. Audio processing such as time shift and time scaling modification can cause displacement between embedding and detection process and it is hence difficult for watermark to survive. The pitch detection is done using the coefficients of wavelet transform of the host audio, which is used as the synchronization point. Before watermark detection, the detector finds the synchronization point to realize the synchronization between the watermark embedding and detection process. Recently, blind source separation by independent component analysis (ICA) has received attention because of its potential applications in signal processing. ICA is adopted during the watermark detection to realize the true blind detection without any information about the host audio, watermark, embedding information and attacks. The accuracy of the watermarking extraction also depends on the statistical independence of original audio and watermark signal. To resist the cheating attacks, we use the one way hashing function before watermark reconstruction. Only t or more honest users can work collectively to reconstruct the secret, while dishonest users could not do it. The detailed performance analysis of the proposed watermarking method is presented. Different audio signals are used to test the robustness of the method. Experimental results show that the proposed audio watermarking technique is robust against many audio processing performed by popular watermark test software-Slirmark, such as low-pass filtering, median filtering, additive Gaussian noise, mp3 compression, time scaling, time shift, dithering and quantization.