基于关键帧提取与DBSCAN的视频内容篡改识别技术

doi:10.13229/j.cnki.jdxbgxb.20241261

Abstract

Abstract:

To solve the problem of a large amount of human resource consumption caused by tampering with video content review and screening， a technique for identifying tampered content in videos is proposed in this paper. First， key frames are extracted by calculating inter-frame differences. Then， the locations of tampering are determined through differential analysis between the key frames and the original frames. Subsequently， based on the phenomenon of location association and semantic aggregation， the problems of location dispersion and semantic dispersion are solved by the DBSCAN algorithm， which is caused by the randomness of tampering with location and content structure. Finally， optical character recognition （OCR） technology is applied to decipher the specific content that has been altered. Spatio-temporal position and content identification of tampered video content are achieved by the proposed method， providing a solid technical foundation for video content inspection in fields such as public safety， media， and business.

Key words: keyframe extraction, SSIM, DBSCAN, morphological transformation, optical character recognition

CLC Number:

TP309.2

Fei REN. Video content tampering identification technology based on key frame extraction and DBSCAN[J].Journal of Jilin University(Engineering and Technology Edition), 2026, 56(2): 555-563.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Table 1

Table 2

References 19

[1]	崔雪冰, 冯巧娟, 崔平非. 基于内容特征的MPEG视频认证方案[J]. 计算机应用, 2010, 30(1): 214-216.
	Cui Xue-bing, Feng Qiao-juan, Cui Ping-fei. MPEG video authentication scheme based on content feature[J]. Journal of Computer Applications, 2010, 30(1): 214-216.
[2]	Sun T F, Jiang X H, Chao J. A novel video inter-frame forgery model detection scheme based on optical flow consistency[C]∥International Workshop on Digital Watermarking, Berlin, Germany, 2012: 261-281.
[3]	张珍珍, 侯建军, 李赵红, 等. 基于MSSIM商一致性的视频插帧和删帧篡改检测[J]. 北京邮电大学学报, 2015, 38(4): 84-88.
	Zhang Zhen-zhen, Hou Jian-jun, Li Zhao-hong, et al. Video-frame insertion and deletion detection based on consistency of quotients of MSSIM[J]. Journal of Beijing University of Posts and Telecommunications, 2015, 38(4): 84-88.
[4]	Lv C H, Huang Y. Effective keyframe extraction from personal video by using nearest neighbor clustering[C]∥11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, Beijing, China, 2018: 1-4.
[5]	Valognes J, Amer M A, Dastjerdi N S. Effective keyframe extraction from RGB and RGB-D video sequences[C]∥Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA), Montreal, Canada, 2017: 1-5.
[6]	Duan L, Xiong D, Lee J, et al. A local density based spatial clustering algorithm with noise[C]∥IEEE International Conference on Systems, Man and Cybernetics, Taipei, China,2006:4061-4066.
[7]	赵黎, 祁卫, 李子青, 等. 基于关键帧提取的最近特征线(NFL)聚类算法的镜头检索方法[J]. 计算机学报, 2000, 23(12): 1292-1296.
	Zhao Li, Qi Wei, Li Zi-qing, et al. Key frame extraction based improved nearest feature line( NFL) classification algorithm[J]. Chinese Journal Computers, 2000, 23(12): 1292-1296.
[8]	秦绪佳, 王慧玲, 杜轶诚, 等. HSV色彩空间的Retinex结构光图像增强算法[J]. 计算机辅助设计与图形学学报, 2013, 25(4): 488-493.
	Qin Xu-jia, Wang Hui-ling, Du Yi-cheng, et al. Structured light image enhancement algorithm based on retinex in hsv color space[J]. Journal of Computer-Aided Design & Computer Graphics, 2013, 25(4):488-493.
[9]	Sabu A M, Das A S. A survey on various optical charac-ter recognition techniques[C]∥Conference on Emerg-ing Devices and Smart Systems (ICEDSS),Tiruchengode, India, 2018: 152-155.
[10]	Sarika N, Sirisala N, Velpuru M S. CNN based optical character recognition and applications[C]∥6th Inter-national Conference on Inventive Computation Technologies, Coimbatore, India, 2021: 666-672.
[11]	Jun L. An improved DBSCAN clustering algorithm[J]. Computer and Communications, 2008, 8: 47468 -47476.
[12]	∥顾益军, 解易, 夏天.基于内容代表性评价的关键帧抽取[J].计算机科学, 2014, 41(8): 286-288.
	Gu Yi-jun, Xie Yi, Xia Tian. Keyframe extraction based on representative evaluation of contents[J]. Computer Science, 2014, 41(8): 286-288.
[13]	Decombas M, Dufaux F, Renan E, et al. A new object based quality metric based on sift and SSIM[C]∥19th IEEE International Conference on Image Processing, Orlando, USA, 2012: 1493-1496.
[14]	Gupta P, Srivastava P, Bhardwaj S, et al. A modified PSNR metric based on HVS for quality assessment of color images[C]∥International Conference on Communication and Industrial Application, Kolkata, India, 2011: 1-4.
[15]	Alain H, Ziou D. Image quality metrics: PSNR vs. SSIM[C]∥20th International Conference on Pattern Recognition, Istanbul, Turkey, 2010: 2366-2369.
[16]	王浩, 张叶, 沈宏海, 等. 图像增强算法综述[J]. 中国光学, 2017, 10(4): 438-448.
	Wang Hao, Zhang Ye, Shen Hong-hai, et al. Review of image enhancement algorithms[J]. Chinese Optics, 2017, 10(4): 438-448.
[17]	金利娜, 于炯, 杜旭升, 等.基于生成对抗网络和变分自编码器的离群点检测算法[J]. 计算机应用研究, 2022, 39(3): 774-779.
	Jin Li-na, Yu Jiong, Du Xu-sheng, et al. Generative adversarial network and variational auto-encoder based outlier detection[J]. Application Research of Computers, 2022, 39(3): 774-779.
[18]	Evans A N. Morphological gradient operators for colour images[C]∥International Conference on Image Processing, Singapore, 2004: 3089-3092.
[19]	Smiti A, Eloudi Z. Soft DBSCAN: improving DBSCAN clustering method using fuzzy set theory[C]∥6th International Conference on Human System Interactions, Sopot, Poland, 2013: 380-385.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

方法	正确检测	误检测	准确率/%	召回率/%	检测时间/min
本文	1 630	165	96.55	90.21	56.84
光流法	2 419	1 701	65.12	54.20	171.67
MSSIM商	3 103	869	91.87	74.25	185.36
聚类法	2 521	226	93.33	91.60	68.68

Video content tampering identification technology based on key frame extraction and DBSCAN

RICH HTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 10

References 19

Related Articles 7

Metrics

Comments

Recommended 0

[1]	Hang ZHANG,Yu SUN,Bao-lin MA,Shi-hao NIU,Xing-yue WANG,Neng-chao LYU. Reliability-based design of length for auxiliary lane at dual-lane highway exits [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(8): 2611-2618.
[2]	Xiao-yan GU,Cheng-long SUI,Xing DI,Zheng-yu MENG,Kai-xuan ZHU,Chang-chun CHU. Effect of welding energy on performance of Cu/Ti joints obtained by ultrasonic welding [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1669-1676.
[3]	Xiao-yan GU,Dong-feng LIU,Jing LIU,Da-qian SUN,Hui-feng MA. Effect of welding energy on microstructure and mechanical properties of Cu/Al joints welded by ultrasonic welding [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1600-1607.
[4]	QIU Xiao-ming, WANG Yin-xue, YAO Han-wei, FANG Xue-qing, XING Fei. Multi-objective optimization of resistance spot welding parameters for DP1180/DP590 using grey relational analysis based Taguchi [J]. 吉林大学学报(工学版), 2018, 48(4): 1147-1152.
[5]	LIU Shu-fen, MENG Dong-xue, WANG Xiao-yan. DBSCAN algorithm based on grid cell [J]. 吉林大学学报(工学版), 2014, 44(4): 1135-1139.
[6]	LV Dan,BI Du-yan. Image quality assessment in DCT domain based on structural similarity [J]. 吉林大学学报(工学版), 2011, 41(6): 1771-1776.
[7]	JIAO Zhu-Jing, XIONG Wei-Li, XU Bao-Guo. Dissimilarsensor data fusion based on weighted least square [J]. 吉林大学学报(工学版), 2010, 40(03): 816-0820.