Using counter-propagation neural network for robust digital audio watermarking in DWT domain

Recently, the watermarking is an important technique to protect copyright, which allows authentic watermark to be hidden in multimedia such as digital image, video and audio. In this paper, we propose a DWT-based Counter-Propagation Neural network (CPN) for digital audio watermarking. The db4 filter of the Daubechies wavelet is applied in this paper. The coefficients obtained from 4-level db4 and the corresponding watermark is used for training the CPN. Different from the traditional methods, the watermark is embedded in the synapses of CPN instead of the original audio signal. In addition, because of the watermark is memorized in the synapses, most of the attacks are not degrade the quality of the extracted watermark image. Moreover, the watermark embedding procedure and extracting procedure are integrated into the proposed CPN. Experimental results show, that the proposed method has capabilities of robustness, inaudibility and authenticity.