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University of Suceava
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Print ISSN: 1582-7445
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doi: 10.4316/AECE


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  3/2019 - 12
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 HIGHLY CITED PAPER 

Spatial Video Forgery Detection and Localization using Texture Analysis of Consecutive Frames

SADDIQUE, M. See more information about SADDIQUE, M. on SCOPUS See more information about SADDIQUE, M. on IEEExplore See more information about SADDIQUE, M. on Web of Science, ASGHAR, K. See more information about  ASGHAR, K. on SCOPUS See more information about  ASGHAR, K. on SCOPUS See more information about ASGHAR, K. on Web of Science, BAJWA, U. I. See more information about  BAJWA, U. I. on SCOPUS See more information about  BAJWA, U. I. on SCOPUS See more information about BAJWA, U. I. on Web of Science, HUSSAIN, M. See more information about  HUSSAIN, M. on SCOPUS See more information about  HUSSAIN, M. on SCOPUS See more information about HUSSAIN, M. on Web of Science, HABIB, Z. See more information about HABIB, Z. on SCOPUS See more information about HABIB, Z. on SCOPUS See more information about HABIB, Z. on Web of Science
 
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Download PDF pdficon (1,374 KB) | Citation | Downloads: 514 | Views: 992

Author keywords
forensics, image classification, machine learning, multimedia systems

References keywords
detection(25), video(23), image(17), forgery(16), processing(15), multimedia(10), digital(10), signal(9), object(8), pattern(7)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-08-31
Volume 19, Issue 3, Year 2019, On page(s): 97 - 108
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.03012
Web of Science Accession Number: 000486574100012
SCOPUS ID: 85072162917

Abstract
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Full text preview
Now-a-days, videos can be easily recorded and forged with user-friendly editing tools. These videos can be shared on social networks to make false propaganda. During the process of spatial forgery, the texture and micro-patterns of the frames become inconsistent, which can be observed in the difference of two consecutive frames. Based on this observation, a method has been proposed for detection of forged video segments and localization of forged frames. Employing the Chrominance value of Consecutive frame Difference (CCD) and Discriminative Robust Local Binary Pattern (DRLBP), a new descriptor is introduced to model the inconsistency embedded in the frames due to forgery. Support Vector Machine (SVM) is used to detect whether the pair of consecutive frames is forged. If at least one pair of consecutive frames is detected as forged, the video segment is predicted as forged and the forged frames are localized. Intensive experiments are performed to validate the performance of the method on a combined dataset of videos, which were tampered by copy-move and splicing methods. The detection accuracy on large dataset is 96.68 percent and video accuracy is 98.32 percent. The comparison shows that it outperforms the state-of-the-art methods, even through cross dataset validation.


References | Cited By  «-- Click to see who has cited this paper

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References Weight

Web of Science® Citations for all references: 36,448 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 810 ACR
SCOPUS® Average Citations per reference: 0

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-03-07 18:59 in 258 seconds.




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