华南理工大学学报(自然科学版)

• 计算机科学与技术 • 上一篇    下一篇

基于基音延迟组内相关性的AMR隐写分析算法

任延珍1 柳登凯1 杨婧2 王丽娜1   

  1. 1. 武汉大学 国家网络安全学院,湖北 武汉 430072; 2. 深圳市国家税务局,广东 深圳 518000
  • 收稿日期:2017-11-15 出版日期:2018-05-25 发布日期:2018-04-03
  • 通信作者: 任延珍( 1973-) ,女,博士,副教授,主要从事多媒体信息隐藏、隐写分析、多媒体取证及内容感知技术等的研究. E-mail:renyz@whu.edu.cn
  • 作者简介:任延珍( 1973-) ,女,博士,副教授,主要从事多媒体信息隐藏、隐写分析、多媒体取证及内容感知技术等的研究.
  • 基金资助:
     国家自然科学基金联合基金资助项目( U1536114, U61872275, U1536204) 

AMR steganalysis algorithm based on intra group correlation of pitch delay

REN Yanzhen1 LIU Dengkai1 YANG Jing2 WANG Lina1   

  1. 1. School of Cyber Science and Engineering,Wuhan University,Wuhan 430072,Hubei,China; 2. Shenzhen Municipal Office,State Administration of Taxation,Shenzhen 518000,Guangdong,China
  • Received:2017-11-15 Online:2018-05-25 Published:2018-04-03
  • Contact: 任延珍( 1973-) ,女,博士,副教授,主要从事多媒体信息隐藏、隐写分析、多媒体取证及内容感知技术等的研究. E-mail:renyz@whu.edu.cn
  • About author:任延珍( 1973-) ,女,博士,副教授,主要从事多媒体信息隐藏、隐写分析、多媒体取证及内容感知技术等的研究.
  • Supported by:
     Supported by the Joints Funds of the National Natural Science Foundation of China( U1536114, U61872275, U1536204)

摘要:

AMR作为移动互联网的语音压缩编码标准被广泛应用,同时也为隐写提供了新的载体。由于基音延迟参数所存在的预测不准确性,现有隐写算法通过对基音延迟参数进行微量调整以隐藏信息。本文对AMR编码算法的基音延迟预测编码特征进行分析,发现了AMR帧内各子帧基音延迟之间相关性的差异,提出基于子帧组内一阶Markov转移概率的隐写分析特征,并与基音延迟二阶差分Markov转移概率特征组合,构建新的隐写分析算法。实验结果表明,在混合训练的环境中,本文算法较现有算法的检测正确率有明显提升,尤其是在低嵌入率情况下,性能提升显著。在10%相对嵌入率的情况下,隐写样本的检测正确率较现有算法提升1%~10%。

关键词: AMR, 基音延迟, Markov转移概率, 隐写分析

Abstract: As a standard of speech compression coding for mobile Internet,AMR has been widely used,and also provides a new carrier for steganography. Due to the inaccurate prediction of pitch delay parameters,the existing steganographic algorithms hide the information by adjusting the pitch delay parameters. In this paper,we find the correlation between the pitch delay of each subframe by analyzing the characteristics of the pitch delay prediction coding of AMR encoding algorithm. Thus,we propose a new steganalysis algorithm which consists of features based on first-order Markov transition probability in subframe group and the second-order differential Markov transition probability feature of pitch delay. The experimental results show that, in the mixed training environment, the accuracy of the proposed algorithm is significantly improved,compared with the existing algorithms,especially in the case of low embedding rate, the performance is improved significantly. In the case of 10% relative embedding rate, the detection accuracy of stego samples is improved by 1% ~10% compared with the existing algorithms. 

Key words: AMR, pitch delay, Markov transition probability, steganalysis

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