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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  3/2019 - 12
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 HIGH-IMPACT 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: 1,064 | Views: 2,047

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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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.


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Cited-By CrossRef

[1] Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment, Vinolin, V., Sucharitha, M., The Visual Computer, ISSN 0178-2789, Issue 8, Volume 37, 2021.
Digital Object Identifier: 10.1007/s00371-020-01992-5
[CrossRef]

[2] A comprehensive survey on passive techniques for digital video forgery detection, Shelke, Nitin Arvind, Kasana, Singara Singh, Multimedia Tools and Applications, ISSN 1380-7501, Issue 4, Volume 80, 2021.
Digital Object Identifier: 10.1007/s11042-020-09974-4
[CrossRef]

[3] Classification of Authentic and Tampered Video Using Motion Residual and Parasitic Layers, Saddique, Mubbashar, Asghar, Khurshid, Bajwa, Usama Ijaz, Hussain, Muhammad, Aboalsamh, Hatim A., Habib, Zulfiqar, IEEE Access, ISSN 2169-3536, Issue , 2020.
Digital Object Identifier: 10.1109/ACCESS.2020.2980951
[CrossRef]

[4] Digital Video Tampering Detection and Localization: Review, Representations, Challenges and Algorithm, Akhtar, Naheed, Saddique, Mubbashar, Asghar, Khurshid, Bajwa, Usama Ijaz, Hussain, Muhammad, Habib, Zulfiqar, Mathematics, ISSN 2227-7390, Issue 2, Volume 10, 2022.
Digital Object Identifier: 10.3390/math10020168
[CrossRef]

[5] Real time object-based video forgery detection using YOLO (V2), Raskar, Punam Sunil, Shah, Sanjeevani Kiran, Forensic Science International, ISSN 0379-0738, Issue , 2021.
Digital Object Identifier: 10.1016/j.forsciint.2021.110979
[CrossRef]

[6] A comprehensive survey of image and video forgery techniques: variants, challenges, and future directions, Nabi, Syed Tufael, Kumar, Munish, Singh, Paramjeet, Aggarwal, Naveen, Kumar, Krishan, Multimedia Systems, ISSN 0942-4962, Issue 3, Volume 28, 2022.
Digital Object Identifier: 10.1007/s00530-021-00873-8
[CrossRef]

[7] Frame Identification of Object-Based Video Tampering Using Symmetrically Overlapped Motion Residual, Kim, Tae Hyung, Park, Cheol Woo, Eom, Il Kyu, Symmetry, ISSN 2073-8994, Issue 2, Volume 14, 2022.
Digital Object Identifier: 10.3390/sym14020364
[CrossRef]

[8] Spatiotemporal Trident Networks: Detection and Localization of Object Removal Tampering in Video Passive Forensics, Yang, Quanxin, Yu, Dongjin, Zhang, Zhuxi, Yao, Ye, Chen, Linqiang, IEEE Transactions on Circuits and Systems for Video Technology, ISSN 1051-8215, Issue 10, Volume 31, 2021.
Digital Object Identifier: 10.1109/TCSVT.2020.3046240
[CrossRef]

[9] Optical flow and pattern noise-based copy–paste detection in digital videos, Singh, Raahat Devender, Aggarwal, Naveen, Multimedia Systems, ISSN 0942-4962, Issue 3, Volume 27, 2021.
Digital Object Identifier: 10.1007/s00530-020-00749-3
[CrossRef]

[10] The Detection and Classification of Microcalcifications in the Visibility-Enhanced Mammograms Obtained by using the Pixel Assignment-Based Spatial Filter, HEKIM, M., AYDIN YURDUSEV, A., ORAL, C., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 4, Volume 19, 2019.
Digital Object Identifier: 10.4316/AECE.2019.04009
[CrossRef] [Full text]

[11] A Comparative Study of Deepfake Video Detection Method, Ramadhani, Kurniawan Nur, Munir, Rinaldi, 2020 3rd International Conference on Information and Communications Technology (ICOIACT), ISBN 978-1-7281-7356-6, 2020.
Digital Object Identifier: 10.1109/ICOIACT50329.2020.9331963
[CrossRef]

[12] Video Forgery Detection using CNN, Koshy, Litty, S, Ajay, Paul, Akhil, V, Hariharan, Basheer, Ashil, 2021 Smart Technologies, Communication and Robotics (STCR), ISBN 978-1-6654-1806-5, 2021.
Digital Object Identifier: 10.1109/STCR51658.2021.9588860
[CrossRef]

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