基于人工神经网络的数字音频水印算法

提出了一种新的基于神经网络训练学习的数字音频水印算法 ,采用本算法在一段数字音频数据中隐藏了一幅不可感知的二值图像 .通过后向传播算法的神经网络训练出模板信号与嵌入了水印信号的音频之间的关系特征 ,由于神经网络具有学习和自适应的特性 ,通过训练后的神经网络几乎能够完全恢复嵌入到音频中的水印数据 .通过仿真实验结果表明该算法具有较好的鲁棒性和抵抗常用的信号处理方法的处理的能力 ,特别是在水印检测时不需要原始的音频信号 .

Digital Audio Watermarking Based on Artificial Neural Networks

Proposed novel digital watermarking technique based on neural networks for audio signal, Our watermarking technique hides a imperceptible image into an audio, and then uses neural networks to learn the characteristics of the embedded watermark related to the watermarked audio. Due to the neural networks possessing the learning and adaptive capabilities, the trained artificial neural networks almost exactly recover the watermark from the watermarked audio. The watermarking method does not require the use of the original signal for watermarking detection. The experimental results show that the embedded watermark is robust to audio signal processing. such as MPEG audio coding, cropping, filtering, resampling, and requantization.