采用量子神经网络的音频水印新算法

提出了一种采用小波变换和量子神经网络的音频数字水印算法。首先对分帧的音频信号进行小波分解，利用量子神经网络将音频信号的小波低频系数映射为数字水印；然后利用分类准确小波低频系数替换少量分类模糊的小波低频系数，提高水印检测正确率。实验结果表明，通过合理选择替换门限，可以提高算法的鲁棒性，有效抵御噪声、低通滤波、重采样、重量化等攻击。在无门限条件下，相比BP神经网络的水印检测正确率平均提高约1%。 

The new audio watermarking algorithm using quantum neural networks

A novel audio watermarking scheme using wavelet decomposition and quantum neural networks (QNN) is proposed in this paper. Firstly, with the wavelet decomposition of the framed audio signal, the low frequency wavelet coefficients are mapped to the watermarking by QNN train. Then, the low frequency wavelet coefficients classified fuzzily are replaced with the ones classified accurately. In this way, the correct rate of the watermarking detection can be improved. Experimental results show that, by reasonable selection of threshold, the watermarking is robust against some different attacks effectively, such as noise adding, low-pass filtering, re-sampling and re-quantizing. Compared with BP neural networks, the correct rates of the QNN algorithm can be increased by an average of 1% without threshold.