全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Authentication of Video Evidence for Forensic Investigation: A Case of Nigeria

DOI: 10.4236/jis.2021.122008, PP. 163-176

Keywords: Inter-Frame, Video Forgery, Correlation Coefficients, Forensic Investigation, Threshold

Full-Text   Cite this paper   Add to My Lib

Abstract:

Video shreds of evidence are usually admissible in the court of law all over the world. However, individuals manipulate these videos to either defame or incriminate innocent people. Others indulge in video tampering to falsely escape the wrath of the law against misconducts. One way impostors can forge these videos is through inter-frame video forgery. Thus, the integrity of such videos is under threat. This is because these digital forgeries seriously debase the credibility of video contents as being definite records of events. This leads to an increasing concern about the trustworthiness of video contents. Hence, it continues to affect the social and legal system, forensic investigations, intelligence services, and security and surveillance systems as the case may be. The problem of inter-frame video forgery is increasingly spontaneous as more video-editing software continues to emerge. These video editing tools can easily manipulate videos without leaving obvious traces and these tampered videos become viral. Alarmingly, even the beginner users of these editing tools can alter the contents of digital videos in a manner that renders them practically indistinguishable from the original content by mere observations. This paper, however, leveraged on the concept of correlation coefficients to produce a more elaborate and reliable inter-frame video detection to aid forensic investigations, especially in Nigeria. The model employed the use of the idea of a threshold to efficiently distinguish forged videos from authentic videos. A benchmark and locally manipulated video datasets were used to evaluate the proposed model. Experimentally, our approach performed better than the existing methods. The overall accuracy for all the evaluation metrics such as accuracy, recall, precision and F1-score was 100%. The proposed method implemented in the MATLAB programming language has proven to effectively detect inter-frame forgeries.

References

[1]  Iorliam, A. (2016) Application of Power Laws to Biometrics, Forensics and Network Traffic Analysis. Doctoral Dissertation, University of Surrey, Guildford.
[2]  Wu, Y., Jiang, X., Sun, T. and Wang, W. (2014) Exposing Video Inter-Frame Forgery Based on Velocity Field Consistency. 2014 IEEE International Conference on Acoustics, Speech and Signal Processing, Florence, 4-9 May 2014, 2674-2678.
https://doi.org/10.1109/ICASSP.2014.6854085
[3]  Chao, J., Jiang, X. and Sun, T. (2012) A Novel Video Inter-Frame Forgery Model Detection Scheme Based on Optical Flow Consistency. International Workshop on Digital Watermarking, Shanghai, 31 October-3 November 2012, 267-281.
https://doi.org/10.1007/978-3-642-40099-5_22
[4]  Wang, Q., Li, Z., Zhang, Z. and Ma, Q. (2014) Video Inter-Frame Forgery Identification Based on the Consistency of Correlation Coefficients of Grey Values. Journal of Computer and Communications, 2, 51-57.
https://doi.org/10.4236/jcc.2014.24008
[5]  Zheng, L., Sun, T. and Shi, Y.Q. (2014) Inter-Frame Video Forgery Detection Based on Block-Wise Brightness Variance Descriptor. International Workshop on Digital Watermarking, Taipei, 1-4 October, 18-30.
https://doi.org/10.1007/978-3-319-19321-2_2
[6]  Li, Z., Zhang, Z., Guo, S. and Wang, J. (2016) Video Inter-Frame Forgery Identification Based on the Consistency of Quotient of MSSIM. Security and Communication Networks, 9, 4548-4556.
https://doi.org/10.1002/sec.1648
[7]  Kingra, S., Aggarwal, N. and Singh, R.D. (2017) Inter-Frame Forgery Detection in H. 264 Videos Using Motion and Brightness Gradients. Multimedia Tools and Applications, 76, 25767-25786.
https://doi.org/10.1007/s11042-017-4762-2
[8]  Liu, Y. and Huang, T. (2017) Exposing Video Inter-Frame Forgery by Zernike Opponent Chromaticity Moments and Coarseness Analysis. Multimedia Systems, 23, 223-238.
https://doi.org/10.1007/s00530-015-0478-1
[9]  Aghamaleki, J.A. and Behrad, A. (2017) Malicious Inter-Frame Video Tampering Detection in MPEG Videos Using Time and Spatial Domain Analysis of Quantization Effects. Multimedia Tools and Applications, 76, 20691-20717.
https://doi.org/10.1007/s11042-016-4004-z
[10]  Fadl, S.M., Han, Q. and Li, Q. (2018) Inter-Frame Forgery Detection Based on Differential Energy of Residue. IET Image Processing, 13, 522-528.
https://doi.org/10.1049/iet-ipr.2018.5068
[11]  Zhao, D.N., Wang, R.K. and Lu, Z.M. (2018) Inter-Frame Passive-Blind Forgery Detection for Video Shot Based on Similarity Analysis. Multimedia Tools and Applications, 77, 25389-25408.
https://doi.org/10.1007/s11042-018-5791-1
[12]  Bakas, J., Bashaboina, A.K. and Naskar, R. (2018) MPEG Double Compression Based Intra-Frame Video Forgery Detection using CNN. 2018 International Conference on Information Technology, Bhubaneswar, 19-21 December 2018, 221-226.
https://doi.org/10.1109/ICIT.2018.00053
[13]  Sitara, K. and Mehtre, B.M. (2018) Detection of Inter-Frame Forgeries in Digital Videos. Forensic Science International, 289, 186-206.
https://doi.org/10.1016/j.forsciint.2018.04.056
[14]  Edafioka, L. (2016) An Unspoken Menace: Sexual Harassment in Nigerian Universities.
https://wildaf-ao.org/index.php/en/woman-news/news/312-an-unspoken-menace-sexual-harassment-in-Nigerian-universities
[15]  Abdulaziz, A. (2019) Kano Govt Revokes Contracts of the Contractor Who Filmed Ganduje Bribe Videos.
https://www.premiumtimesng.com/news/headlines/306538-kano-govt-revokes-contracts-of-contractor-who-filmed-ganduje-bribe-videos.html
[16]  Mwai, P. (2020) Nigeria Sars Protest: The Misinformation Circulating Online. BBC Reality Check.
https://www.bbc.com/news/world-africa-54628292
[17]  Anyebe, P.A. (2019) Appraisal of Admissibility of Electronic Evidence in Legal Proceedings in Nigeria. Journal of Law, Policy and Globalization, 92, 1-12.
[18]  Nguyen, X.H. and Hu, Y. (2020) VIFFD—A Dataset for Detecting Video Inter-Frame Forgeries. Vision 6, Mendeley Data.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133