音频隐写方法综述:从传统到深度学习  

An Overview of Audio Steganography Methods:From Tradition to Deep Learning

作　　者：张雄伟 葛晓义 孙蒙 宋宫琨琨 李莉 ZHANG Xiongwei;GE Xiaoyi;SUN Meng;SONG GONG Kunkun;LI Li(College of Command&Control Engineering,Army Engineering University of PLA,Nanjing 210007,China)

机构地区：[1]陆军工程大学指挥控制工程学院,南京210007

出　　处：《数据采集与处理》2023年第5期995-1016,共22页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(62071484,62371469);江苏省优秀青年基金(BK20180080)。

摘　　要：数字音频作为网络空间中广泛应用的媒体,是承载秘密信息的良好载体,常被用来构建实时性强、复杂度低、不可感知性好的隐蔽通信。音频隐写作为确保网络信息安全和数据保密通信的关键技术手段之一,正受到越来越多学者的关注。本文对音频隐写方法的发展脉络进行了系统性梳理。首先,介绍了音频隐写的基本内容,对问题描述、常用数据格式、工具和评价指标等进行总结。其次,按照嵌入域的不同,将传统音频隐写方法分为时域方法、变换域方法和压缩域方法,并分析其优缺点;根据隐写载体的不同,将基于深度学习的隐写方法划分为嵌入载体式、生成载体式和无载体式音频隐写,并对这3种音频隐写方法进行了对比分析。最后,指出了当前音频隐写进一步的研究方向。As a widely used medium in the cyberspace,digital audio serves as an excellent cover for carrying secret information and is often employed in the construction of covert communication systems that prioritize real⁃time performance,low complexity,and imperceptibility.Audio steganography,one of the key techniques for ensuring network information security and confidential communication,has attracted increasing attention from scholars.This paper presents a systematic review of the development context of audio steganography methods.Firstly,we introduce the basic contents of audio steganography,and summarize the problem description,evaluation indicators,common data formats,and tools.Secondly,according to different embedding domains,traditional audio steganography methods are classified into time domain methods,transform domain methods and compression domain methods,and their advantages and disadvantages are analyzed.Furthermore,based on different steganographic covers,the deep learning⁃based steganography methods are categorized into embedding cover⁃based,generating cover⁃based,and coverless audio steganography,then the three steganography methods are compared and analyzed.Finally,suggestions for further research directions in audio steganography are pointed out.

关 键 词：音频隐写 信息隐藏 隐蔽通信 深度学习 短时傅里叶变换 

分 类 号：TN912[电子电信—通信与信息系统]