基于设备本底噪声频谱特征的手机来源识别

doi:10.11959/j.issn.1000-0801.2017019

电信科学 ›› 2017, Vol. 33 ›› Issue (1): 85-94.doi: 10.11959/j.issn.1000-0801.2017019

基于设备本底噪声频谱特征的手机来源识别

裴安山,王让定(),严迪群

宁波大学，浙江宁波 315211

修回日期:2017-01-10 出版日期:2017-01-01 发布日期:2017-06-04
作者简介:裴安山（1992-），男，宁波大学信息科学与工程学院硕士生，主要研究方向为多媒体通信、信息安全、移动终端来源检测等。|王让定（1962-），男，博士，宁波大学高等技术研究院教授，主要研究方向为多媒体通信与取证、信息隐藏与隐写分析、智能抄表及传感网络技术等。|严迪群（1979-），男，博士，宁波大学信息科学与工程学院副教授，主要研究方向为多媒体通信、信息安全、基于深度学习的数字语音取证等。
基金资助:
国家自然科学基金资助项目(61672302);国家自然科学基金资助项目(61300055);浙江省自然科学基金资助项目(LZ15F020002);浙江省自然科学基金资助项目(LY17F020010);宁波大学科研基金资助项目(XKXL1405);宁波大学科研基金资助项目(XKXL1420);宁波大学科研基金资助项目(XKXL1509);宁波大学科研基金资助项目(XKXL1503);宁波大学科研创新基金资助项目(G16079);宁波大学王宽诚幸福基金资助项目

Cell-phone origin identification based on spectral features of device self-noise

Anshan PEI,Rangding WANG(),Diqun YAN

Ningbo University,Ningbo 315211,China

Revised:2017-01-10 Online:2017-01-01 Published:2017-06-04
Supported by:
The National Natural Science Foundation of China(61672302);The National Natural Science Foundation of China(61300055);Natural Science Foundation of Zhejiang Province of China(LZ15F020002);Natural Science Foundation of Zhejiang Province of China(LY17F020010);Scientific Research Foundation of Ningbo University(XKXL1405);Scientific Research Foundation of Ningbo University(XKXL1420);Scientific Research Foundation of Ningbo University(XKXL1509);Scientific Research Foundation of Ningbo University(XKXL1503);Scientific Research Foundation of Graduate School of Ningbo University(G16079);K.C.Wong Magna Fund in Ningbo University

摘要/Abstract

摘要：

随着手机录音设备的普及以及各种功能强大且易于操作的数字媒体编辑软件的出现，手机来源识别已成为多媒体取证领域的热点问题。将本底噪声作为手机的“指纹”，提出了一种基于本底噪声的手机来源识别方法。该方法先通过静音段录音的估计得到本底噪声；然后计算本底噪声的频谱特征在时间轴方向上的均值，将其作为手机来源识别的分类特征；最后采用主成分分析（PCA）法对特征进行降维，并采用支持向量机（SVM）进行分类。实验部分对24款主流型号的手机进行了分类，结果表明本文方法的平均识别准确率（accuracy）和平均召回率（recall）达到了99.24%，同时也验证了相比MFCC，本底噪声有更加优越的性能。

关键词: 多媒体取证, 手机来源识别, 本底噪声, 频谱特征

Abstract:

With the widespread availability of cell-phone recording devices and the availability of various powerful and easy-to-use digital media editing software,source cell-phone identification has become a hot topic in multimedia forensics.A novel cell-phone identification method was proposed based on the recorded speech.Firstly,device self-noise (DSN) was considered as the fingerprint of the cell-phone and estimated from the silent segments of the speech.Then,the mean of the noise's spectrum was extracted as the identification.Principal components analysis (PCA) was applied to reduce the feature dimension.Support vector machine (SVM) was adopted as the classifier to determine the source of the detecting speech.Twenty-four popular models of the cell-phones were evaluated in the experiment.The experimental results show that the average identification accuracy and recall of the method can reach up to 99.24% and demonstrate that the self-noise feature has more superior performance than the MFCC feature.

Key words: multimedia forensics, cell-phone origin identification, self-noise, spectral feature

中图分类号:

TP391

裴安山,王让定,严迪群. 基于设备本底噪声频谱特征的手机来源识别[J]. 电信科学, 2017, 33(1): 85-94.

Anshan PEI,Rangding WANG,Diqun YAN. Cell-phone origin identification based on spectral features of device self-noise[J]. Telecommunications Science, 2017, 33(1): 85-94.

图/表 10

图1

图2

表1

图3

图4

图5

图6

表2

表3

表4

参考文献 19

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H1	HTC	D610t
H2		D820t
H3		One M7
W1	华为	Honor6
W2		Honor7
W3		Mate7
O1	OPPO	Find7
O2		Oneplus1
O3		R831S
S1	三星	Galaxy Note2
S2		Galaxy S5
S3		Galaxy GT-I8558
A1	苹果	iPhone 4s
A2		iPhone 5
A3		iPhone 5s
A4		iPhone 6
A5		iPhone 6s
Z1	魅族	Meilan Note
Z2		MX2
Z3		MX4
M1	小米	Mi 3
M2		Mi 4
M3		红米Note1
M4		红米 Note2

标签												预测标签
标签	H₁	H₂	H₃	W₁	W₂	W₃	A₁	A₂	A₃	A₄	A₅	Z₁	Z₂	Z₃	M₁	M₂	M₃	M₄	O₁	O₂	O₃	S₁	S₂	S₃
H₁	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
H₂	2.5%	97.5%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
H₃	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
W₁	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
W₂	0	0	0	2.5%	97.5%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
W₃	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₁	0	0	0	0	0	0	97.5%	0	0	1.7%	0	0	0.8%	0	0	0	0	0	0	0	0	0	0	0
A₂	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₃	0	0	0	0	0	0	0	0	98.3%	1.7%	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₄	0	0	0	0	0	0	2.5%	0	5.8%	91.7%	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₅	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0
Z₁	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0
Z₂	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0
Z₃	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0
M₁	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0
M₂	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0
M₃	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0
M₄	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	99.2%	0.8%	0	0	0	0	0
O₁	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0
O₂	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0
O₃	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0
S₁	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0
S₂	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0
S₃	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%

标签												预测标签
标签	H₁	H₂	H₃	W₁	W₂	W₃	A₁	A₂	A₃	A₄	A₅	Z₁	Z₂	Z₃	M₁	M₂	M₃	M₄	O₁	O₂	O₃	S₁	S₂	S₃
H₁	97%	1%	0	0	2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
H₂	2%	98%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
H₃	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
W₁	0	0	0	97%	3%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
W₂	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
W₃	0	0	0	0	1%	99%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₁	0	0	0	0	0	0	99%	0	0	1%	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₂	0	0	0	0	2%	0	0	97%	1%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₃	0	0	0	0	0	0	1%	3%	94%	2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₄	0	0	0	0	0	0	5%	0	0	95%	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₅	0	0	0	0	0	0	1%	0	0	0	97%	0	2%	0	0	0	0	0	0	0	0	0	0	0
Z₁	0	0	0	0	1%	0	0	0	0	0	0	98%	0	0	0	0	0	0	0	1%	0	0	0	0
Z₂	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0
Z₃	0	0	0	0	0	0	0	0	0	0	0	1%	2%	97%	0	0	0	0	0	0	0	0	0	0
M₁	0	0	0	0	0	0	0	0	0	0	0	0	1%	0	99%	0	0	0	0	0	0	0	0	0
M₂	0	0	0	0	0	0	0	0	1%	0	0	0	0	0	0	99%	0	0	0	0	0	0	0	0
M₃	0	0	0	0	1%	0	0	0	0	0	0	0	0	0	0	0	99%	0	0	0	0	0	0	0
M₄	0	0	0	0	1%	0	0	0	0	0	0	0	0	0	0	0	0	99%	0	0	0	0	0	0
O₁	0	0	0	0	0	0	0	0	0	0	0	0	1%	0	0	0	0	0	99%	0	0	0	0	0
O₂	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0
O₃	0	0	0	0	0	0	0	0	0	0	0	0	2%	0	0	0	0	0	0	0	98%	0	0	0
S₁	0	0	0	0	0	0	0	0	0	0	0	0	2%	0	0	0	0	0	1%	0	0	97%	0	0
S₂	0	0	0	0	3%	0	0	0	0	0	0	0	1%	0	0	0	0	0	0	0	0	0	96%	0
S₃	0	0	0	0	0	0	0	0	0	0	0	0	1%	0	0	0	0	0	0	0	0	0	0	99%

标签												预测标签
标签	H₁	H₂	H₃	W₁	W₂	W₃	A₁	A₂	A₃	A₄	A₅	Z₁	Z₂	Z₃	M₁	M₂	M₃	M₄	O₁	O₂	O₃	S₁	S₂	S₃
H₁	50%	49.2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.8%	0	0	0	0	0	0
H₂	20.8%	79.2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
H₃	2.5%	0	96.7%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.8%	0	0	0	0	0
W₁	0	0	0	96.7%	1.7%	0.8%	0	0	0	0	0	0	0	0	0	0.8%	0	0	0	0	0	0	0	0
W₂	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
W₃	0	0	0	4.2%	0	94.2%	0	0	0	0	0	0	0.8%	0	0	0.8%	0	0	0	0	0	0	0	0
A₁	0	0	0	0	0	0	62.5%	0.8%	5%	31.7%	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₂	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₃	0	0	0	0	0	0	15%	1.7%	75.8%	7.5%	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₄	0	0	0	0	0	0	21.7%	0.8%	10.8%	66.7%	0	0	0	0	0	0	0	0	0	0	0	0	0	0
A₅	0	0	0	0	0	0	0	0	0	0	100%	0	0	0	0	0	0	0	0	0	0	0	0	0
Z₁	0	0	0	0	0	0	0	0	0	0	0	85%	0	15%	0	0	0	0	0	0	0	0	0	0
Z₂	0	0	0.8%	1.7%	0	0	0	0	0	0	0	0	97.5%	0	0	0	0	0	0	0	0	0	0	0
Z₃	2.5%	0	0	0	0	0	0	0	0	0	0	0	0	96.7%	0	0	0	0	0	0.8%	0	0	0	0
M₁	0	0	0	0	0	0	0	0	0	0	0	0	0	0	99.2%	0	0	0	0.8%	0	0	0	0	0
M₂	0	0	0	2.5%	0	0.8%	0	0	0	0	0	0	0	0	0	96.7%	0	0	0	0	0	0	0	0
M₃	0	0	1.7%	0	0	0	0	0	0	0	0	0	0	0	0	0	97.5%	0	0	0	0.8%	0	0	0
M₄	10%	1.7%	5.8%	0	0	0.8%	0	0	0	0	0	0	0.8%	4.2%	0	0	0	75%	0	0	0	1.7%	0	0
O₁	0	0	0	0	0	0	0	0	0	0	0	0	2.5%	0.8%	0.8%	0	0	0.8%	87.5%	5.8%	0	0	0	1.7%
O₂	0	0	0	0	0	0	0	0	0	0	0	0	0	0.8%	0	0	0	0	0	93.3%	0	0	0	5.8%
O₃	0	0	2.5%	0	0	0	0	0	0	0	0.8%	0	0	0	0	0	4.2%	0	0	0	92.5%	0	0	0
S₁	5%	0	4.2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	5%	4.2%	0	0	80.8%	0	0.8%
S₂	0	0	0%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	100%	0
S₃	0.8%	0	4.2%	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	0.8%	0	0	0	94.2%

基于设备本底噪声频谱特征的手机来源识别

Cell-phone origin identification based on spectral features of device self-noise

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参考文献 19

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