摘要
回声隐藏是一种以音频为载体的隐写技术,目前针对回声隐藏的隐写分析方法主要以倒谱系数作为手工特征进行分类。然而,这些传统方法普遍在回声幅度较低时检测性能不高。针对回声幅度较低的情况,提出一种基于卷积神经网络(CNN)的回声隐藏隐写分析方法。首先利用短时傅里叶变换(STFT)提取音频的幅度谱系数矩阵作为浅层特征,然后设计了一个卷积神经网络框架对浅层特征进行进一步的深度特征提取,网络框架中包含了四个卷积模块以及三层全连接层,最后分类结果以Softmax进行输出。在三种经典的回声隐藏算法上对提出的方法进行了隐写分析实验评估,实验结果表明,该方法在低回声幅度条件下的检测率分别为98.62%、98.53%和93.20%,与目前所提出的传统基于手工特征的方法和基于深度学习的方法相比,检测性能提升10%以上。
Echo hiding is a steganographic technique with audio as carrier.Currently,the steganalysis methods for echo hiding mainly use the cepstral coefficients as handcrafted-features to realize classification.However,when the echo amplitude is low,the detection performance of these traditional methods is not high.Aiming at the low echo amplitude condition,a steganalysis method for echo hiding based on Convolutional Neural Network(CNN)was proposed.Firstly,Short-Time Fourier Transform(STFT)was used to extract the amplitude spectrum coefficient matrix as the shallow feature.Secondly,the deep feature was extracted by the designed CNN framework from the shallow feature.The network framework consisted of four convolutional blocks and three fully connected layers.Finally,the classification results were output by Softmax.The proposed method was steganographically evaluated on three classic echo hiding algorithms.Experimental results indicate that the detection rates of the proposed method under low echo amplitude are 98.62%,98.53%and 93.20%respectively.Compared with the existing traditional handcrafted-features based methods and deep learning based methods,the proposed method has the detection performance improved by more than 10%.
作者
王杰
王让定
严迪群
林昱臻
WANG Jie;WANG Rangding;YAN Diqun;LIN Yuzhen(Faculty of Electical Engineering and Computer Science,Ningbo University,Ningbo Zhejiang 315211,China)
出处
《计算机应用》
CSCD
北大核心
2020年第2期375-380,共6页
journal of Computer Applications
基金
国家自然科学基金资助项目(U1736215,61672302)
浙江省自然科学基金资助项目(LZ15F020002,LY17F020010)
宁波市自然科学基金资助项目(2017A610123)
宁波大学学科基金资助项目(XKXL1509,XKXL1503)
浙江省移动网应用技术重点实验室开放基金资助项目(F2018001)~~
关键词
回声隐藏
隐写分析
卷积神经网络
短时傅里叶变换
深度学习
echo hiding
steganalysis
Convolutional Neural Network(CNN)
Short-Time Fourier Transform(STFT)
deep learning
