基于非负张量分解的视频篡改检测方法

doi:10.11959/j.issn.2096-109x.2017.00174

Abstract

Abstract:

The authenticity and integrity of video authentication is one of the important contents in information security field.A video tampering detection method based on non-negative tensor decomposition was proposed for video inter-frame tampering.First of all,spectral feature of video frame was extracted quickly.The video was described by a three-dimensional tensor which created by the main compression feature.The tensor was factorized by Tucker non-negative decomposition method and then the time dimension matrix was extracted to calculate correlation.Finally,the tampering position was determined by using the Chebyshev’s inequality.Experiments show that this method can detect the video inter-frame tampering quickly and robustly.

Key words: video tampering detection, nonnegative tensor factorization, Pearson correlation coefficient, discrete cosine transform, Chebyshev’s inequality

CLC Number:

TP393

Xue-li ZHANG,Tian-qiang HUANG,Jing LIN,Wei HUANG. Video tamper detection method based on nonnegative tensor factorization[J]. Chinese Journal of Network and Information Security, 2017, 3(6): 42-49.

Figures/Tables 9

References 12

[1]	胡永健, 刘琲贝, 贺前华 . 数字多媒体取证技术综述[J]. 计算机应用, 2010,30(3): 657-662.
	HU Y J , LIU B B , HE Q H . Review of digital multimedia forensics[J]. Journal of Computer Applications, 2010,30(3): 657-662.
[2]	王俊文, 刘光杰, 张湛 ,等. 基于模式噪声的数字视频篡改取证[J]. 东南大学学报(自然科学版), 2008(S2): 13-17.
	WANG J W , LIU G J , ZHANG Z ,et al. Digital video tampering with evidence based on modal noise[J]. Journal of Southeast University (Natural Science), 2008(S2): 13-17.
[3]	袁秀娟, 黄添强, 陈智文 ,等. 基于纹理特征的数字视频篡改检测[J]. 计算机系统应用, 2012,21(6): 91-95.
	YUAN X J , HUANG T Q , CHEN Z W ,et al. Digital video tamper detection based on texture feature[J]. Computer Systems ＆Applications, 2012,21(6): 91-95.
[4]	黄添强, 陈智文, 苏立超 ,等. 利用内容连续性的数字视频篡改检测[J]. 南京大学学报(自然科学), 2011,47(5): 493-503.
	HUANG T Q , ZHEN Z W,SULC ,et al. Digital video tamper detection with Content continuity[J]. Journal of Nanjing University (Natural Science), 2011,47(5): 493-503.
[5]	YIN L , BAI Z , YANG R . Video forgery detection based on nonnegative tensor factorization[C]// The 4th IEEE International Conference on Information Science and Technology(ICIST). 2014: 148-151.
[6]	KOLDA T G , BADER B W . Tensor decompositions and applications[J]. SIAM Review, 2009,51(3): 455-500.
[7]	TUCKER L R . Some mathematical notes on three-mode factor analysis[J]. Psychometrika, 2006,31(3): 279-311.
[8]	LATHAUWER L D , MOOR B D , VANDEWALLE J . A multilinear singular value decomposition[J]. Siam Journal on Matrix Analysis＆Applications, 2000,21(4): 1253-1278.
[9]	BADER B W , KOLDA T G . Algorithm 862:Matlab tensor classes for fast algorithm prototyping[J]. ACM Transactions on Mathematical Software, 2006,32(4): 635-653.
[10]	林晶, 黄添强, 赖玥聪 ,等. 采用量化离散余弦变换系数检测视频单帧连续多次复制-粘贴篡改[J]. 计算机应用, 2016,36(5): 1356-1361.
	LIN J , HUANG T Q , LAI Y C ,et al. Detect a siagle frame video’s continuous copy and paste by using Quantitative discrete cosine transform coefficients[J]. Compute Application, 2016,36(5): 1356-1361.
[11]	贺忠 . 基于 Lip-切比雪夫方法的数据流异常检测分析[J]. 计算机系统应用, 2009,18(10): 61-64.
	HE Z . The data flow anomaly detection analysis based on LipChebyshev method[J]. Computer System Application, 2009,18(10): 61-64.
[12]	QADIR G , YAHAYA S , HO A T S . Surrey university library for forensic analysis(SULFA) of video content[C]// The 2012 IET Conference on Image Processing. 2012: 1-6.

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编号	视频来源	视频分辨率	篡改操作	检测结果	检测速率/(秒·帧) ^?1
1	Canon EOS 6D拍摄素材	1 920×1 080	同源视频插入11帧：第53~63帧	第52和第63处发现异常点	0.368 4
2	Canon EOS 6D拍摄素材	1 920×1 080	异源视频插入11帧：第28~38帧	第27和第38处发现异常点	0.392 3
3	Canon EOS 6D拍摄素材	1 920×1 080	删除第26帧以后的20帧	第26处发现异常点	0.369 5
4	Canon EOS 6D拍摄素材	1 920×1 080	删除第120帧以后的40帧	第120处发现异常点	0.368 7
5	Canon EOS 6D拍摄素材	1 280×720	同源视频插入9帧：第12~20帧	第11和第20处发现异常点	0.376 5
6	Canon EOS 6D拍摄素材	1 280×720	异源视频插入4帧：第26~29帧	第25和第29处发现异常点	0.244 2
7	Canon EOS 6D拍摄素材	1 280×720	删除第64帧以后的30帧	第64处发现异常点	0.214 6
8	Canon EOS 6D拍摄素材	640×480	同源视频插入7帧：第94~100帧	第93和第100处发现异常点	0.185 5
9	Canon EOS 6D拍摄素材	640×480	异源视频插入11帧：第420~430帧	第419和第430处发现异常点	0.195 9
10	Canon EOS 6D拍摄素材	640×480	删除第83帧以后的20帧	第83处发现异常点	0.170 9
11	SULFA视频库nik_s3000_road(1)	320×240	同源视频插入13帧：第25~37帧	第24和第37处发现异常点	0.172 1
12	SULFA视频库can_220_ball(1)	320×240	异源视频插入22帧：第121~132帧	第120和第132处发现异常点	0.179 2
13	SULFA视频库fuji_2800_busstop(1)	320×240	删除第13帧以后的20帧	第13处发现异常点	0.168 8

算法	precision	recall
本文算法	100%	100%
文献[5]算法	100%	99%

算法	precision	recall
本文算法	93.45%	90.07%
文献[5]算法	83.45%	82.41%

算法	precision	recall
本文算法	90.28%	88.15%
文献[5]算法	88.64%	86.67%

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Video tamper detection method based on nonnegative tensor factorization

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