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Forensic Approach For Object Elimination and Frame Replication Detection Using Noise Based Gaussian Classifier

Govindraj Chittapur, S. Murali, Basavaraj S. Anami , ,
Affiliations
Department of Computer Applications, Basaveshwar Engineering College, Bagalkot India
:10.22362/ijcert/2020/v7/i03/v7i0301


Abstract
In the modern world, people are beliving videos as part of social communication; as camera editing techniques are advanced, video doctoring is a technique for editing and recreating new details in the footage. Identifying these doctored videos poses a problem for the media source, the court of law, and the framework of evidence service. The research on video forensics, and specifically on the automatic recognition of object-based detection of video forgery, is still in its infancy. The approach proposed in this paper uses noise properties, extracted from each frame of the video using Wavelet Transform and nonlinear thresholding such as optimal SURE shrinkage. Gaussian Mixture Density (GMD) uses this as a Gaussian classifier, and the Expectation-Maxima algorithm sets the GMD parameter. Results of the output matrix show that we get excellent precision 99.36 percent recall 99.80 and precision 97.34 percent respectively for object removal and frame duplication detection compared to subsisting methods. The proposed approach effectively detects traces in the forensic video dataset and recognizes these.


Citation
Govindraj Chittapur, S. Murali, Basavaraj S. Anami."Forensic Approach For Object Elimination and Frame Replication Detection Using Noise Based Gaussian Classifier". International Journal of Computer Engineering In Research Trends (IJCERT), ISSN:2349-7084, Vol.7, Issue 03,pp.1-5, March - 2020, URL:http://ijcert.org/ems/ijcert_papers/V7I301.pdf


Keywords : Video Forensic, Wavelet Transform, Expected Maxima, Gaussian Mixture Module, Sulfa, Sysu-Objforge

References
2012). Surrey University Library for Forensic Analysis (SULFA) of video content. 1-6. 10.1049/cp.2012.0422.

[2] S. Chen, S. Tan, B. Li, and J. Huang, "Automatic Detection of Object-Based Forgery in Advanced Video," in IEEE Transactions on Circuits and Systems for Video Technology, vol. 26, no. 11, pp. 2138-2151, Nov. 2016.

[3] Czyzewski, A., Kostek, B., Bratoszewski, P. et al. An audio-visual corpus for multimodal automatic speech recognition. J Intell Inf Syst 49, 167–192 (2017). https://doi.org/10.1007/s10844-016-0438-z

[4] Pandey, Ramesh C., Singh, Sanjay K., and Shukla, K.K. ‘A Passive Forensic Method for Video: Exposing Dynamic Object Removal and Frame Duplication in the Digital Video Using Sensor Noise Features’. Journal of Intelligent & Fuzzy Systems, vol. 32, no. 5, pp. 3339-3353, 2017.

[5] R. Chen, G. Yang, N. Zhu, "Detection of object-based manipulation by the statistical features of object contour," Forensic Science International, vol. 236, pp. 164-169, 2014.

[6] S. Murali, B. S. Anami and G. B. Chittapur, "Detection of Copy-Create Image Forgery Using Luminance Level Techniques," 2011 Third National Conference on Computer Vision, Pattern Recognition, Image Processing, and Graphics, Hubli, Karnataka, 2011, pp. 215-218

[7] S. Tan, S. Chen, and B. Li, "GOP based automatic detection of object-based forgery in the advanced video," 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Hong Kong, 2015, pp. 719-722.

[8] Govindraj Chittapur, S. Murali, and Basavaraj Anami “Video Forgery Detection Using Motion Extractor By Referring Block Matching Algorithm” International Journal of Scientific and Technology Research, Volume 8, Issue 10 2019, PP 3240-3243.  http://www.ijstr.org/final-print/oct2019/Video-Forgery-Detection-Using-Motion-Extractor-By-Referring-Block-Matching-Algorithm.pdf

[9] Chittapur G.B., Murali S., Prabhakara H.S., Anami B.S. (2014) Exposing Digital Forgery in Video by Mean Frame Comparison Techniques. In: Sridhar V., Sheshadri H., Padma M. (eds) Emerging Research in Electronics, Computer Science and Technology. Lecture Notes in Electrical Engineering, vol 248. Springer, New Delhi

[10] Chittapur G.B., Murali S., Prabhakara H.S., Anami B.S. (2019) “Forensic Approach for detecting the region of Copy-Create Video Forgery By applying frame similarity approach” volume7 issue 2, 2019.,pp 12-17 http://www.isca.me/COM_IT_SCI/Archive/v7/i2/3.ISCA-RJCITS-2019-006.pdf

[11] Govindraj Chittapur, S. Murali, Basavaraj S. Anami."Copy Create Video Forgery Detection Techniques Using Frame Correlation Difference by Referring SVM Classifier"., International Journal of Computer Engineering In Research Trends (IJCERT), ISSN:2349-7084, Vol.6, Issue 12,pp.4-8, December- 2019.


DOI Link : https://doi.org/10.22362/ijcert/2020/v7/i03/v7i0301

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  V7I301.pdf


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