基于变分贝叶斯学习的音频水印盲检测方法

唐鑫; 马兆丰; 钮心忻; 杨义先

doi:10.11959/j.issn.1000-436x.2015014

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基于变分贝叶斯学习的音频水印盲检测方法

学术论文 | 更新时间：2024-06-05

- 基于变分贝叶斯学习的音频水印盲检测方法
- Blind audio watermarking mechanism based on variational Bayesian learning
- 通信学报 2015年36卷第1期页码：121-128
- 作者机构：
  
  1. 北京邮电大学信息安全中心，北京100876
  2. 北京国泰信安科技有限公司，北京 100086
- 作者简介：
  
  [ "唐鑫（1987-），男，江苏南京人，北京邮电大学博士生，主要研究方向为数字版权管理、数字内容安全、数字水印等。" ]
  [ "马兆丰（1974-），男，甘肃镇原人，博士，北京邮电大学讲师，主要研究方向为数字版权管理、数字内容安全、计算机网络安全。" ]
  [ "钮心忻（1963-），女，浙江湖州人，北京邮电大学教授、博士生导师，主要研究方向为数字水印、信息隐藏、隐写分析。" ]
  [ "杨义先（1961-），男，四川盐亭人，北京邮电大学教授、博士生导师，主要研究方向为密码学、计算机网络与信息安全。" ]
- 基金信息：
  
  国家自然科学基金资助项目(60803157);国家自然科学基金资助项目(90812001);国家自然科学基金资助项目(61170271);国家自然科学基金资助项目(61272519)
- DOI：10.11959/j.issn.1000-436x.2015014
  中图分类号： TP309
- 网络出版日期：2015-01，
  
  纸质出版日期：2015-01-25
- 稿件说明：
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唐鑫, 马兆丰, 钮心忻, 等. 基于变分贝叶斯学习的音频水印盲检测方法[J]. 通信学报, 2015,36(1):121-128.

Xin TANG, Zhao-feng MA, Xin-xin NIU, et al. Blind audio watermarking mechanism based on variational Bayesian learning[J]. Journal on communications, 2015, 36(1): 121-128.
唐鑫, 马兆丰, 钮心忻, 等. 基于变分贝叶斯学习的音频水印盲检测方法[J]. 通信学报, 2015,36(1):121-128. DOI： 10.11959/j.issn.1000-436x.2015014.

Xin TANG, Zhao-feng MA, Xin-xin NIU, et al. Blind audio watermarking mechanism based on variational Bayesian learning[J]. Journal on communications, 2015, 36(1): 121-128. DOI： 10.11959/j.issn.1000-436x.2015014.

摘要

为了提高音频水印的检测性能，基于音频帧MFCC特征的统计特性，提出了一种音频水印盲检测方法。在音频帧的DCT系数上嵌入扩频水印，对嵌入水印的音频帧和原始音频帧分别提取MFCC特征进行训练，分别建立高斯混合模型，并通过变分贝叶斯学习方法估计出高斯混合模型的参数，检测时依据最大似然的原则。实验结果显示提出的方法在音频信号受到噪声干扰和恶意攻击的情况下，相对基于EM算法的方法在误检率上有明显降低，在小样本训练情况下具有更好的效果并且可以有效避兔过拟合的问题。

Abstract

In order to improve the performance of audio watermarking detection

a blind audio watermarking mechanism using the statistical characteristics based on MFCC features of audio frames was proposed.The spread spectrum watermarking was embedded in the DCT coefficients of audio frames.MFCC features extracted from watermarked audio frames as well as un-watermarked ones were trained to establish their Gaussian mixture models and to estimate the parameters by vatiational Bayesian learning method respectively.The watermarking was detected according to the maximum likelihood principle.The experimental results show that our method can lower the false detection rate compared with the method using EM algorithm when the audio signal was under noise and malicious attacks.Also

the experiments show that the proposed method achieves better performance in handling insufficient training data as well as getting rid of over-fitting problem.

关键词

Keywords

references

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