融合空间约束和梯度结构信息的视频篡改检测算法

doi:10.11959/j.issn.2096-109x.2019052

Abstract

Abstract:

The traditional video passive forensics method using only the principle of similarity between adjacent frames will cause a lot of false detection for the video with severe motion.Aiming at this problem,a video tamper detection method combining spatial constraints and gradient structure information was proposed.Firstly,the low motion region and the high texture region were extracted by using spatial constraint criteria.The two regions were merged to obtain the robust quantitative correlation rich regions for extracting video optimal similarity features.Then improving the extraction and description methods of the original features,and using the similarity of the gradient structure in accordance with the characteristics of the human visual system to calculate the spatial constraint correlation value.Finally,the tampering points were located by the Chebyshev inequality.Experiments show that the proposed algorithm has lower false detection rate and higher accuracy.

Key words: spatial constraints, the quantitative correlation rich regions, GSSIM（gradient structure similarity）, videos with severe motion

CLC Number:

TP393

Han PU, Tianqiang HUANG, Bin WENG, Hui XIAO, Wei HUANG. Video tampering detection algorithm based on spatial constraint and gradient structure information[J]. Chinese Journal of Network and Information Security, 2019, 5(5): 64-79.

Figures/Tables 16

References 17

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篡改帧数	篡改点总数	检测出的正确点数	未检测出的篡改点数	检测出的错误点数	召回率	精确率
5	200	168	32	6	84.00%	96.55%
10	200	176	24	5	88.00%	97.24%
20	200	178	22	4	89.00%	97.80%
25	200	179	21	6	89.50%	96.76%
50	200	183	27	8	91.50%	95.81%
75	200	184	16	7	92.00%	96.34%
100	200	189	11	5	94.50%	97.42%

数据集	本文算法		文献[5]算法		文献[6]算法
数据集	准确率	召回率	准确率	召回率	准确率	召回率
SULFA	99.95%	99.45%	82.43%	85.26%	89.02%	90.53%
CDNET	84.62%	91.11%%	81.65%	83.31%	81.83%	83.71%
HUANAN	99.71%	86.25%	83.12%	84.21%	84.91%	85.72%

Video tampering detection algorithm based on spatial constraint and gradient structure information

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