|3/2019 - 12|
Spatial Video Forgery Detection and Localization using Texture Analysis of Consecutive FramesSADDIQUE, M. , ASGHAR, K. , BAJWA, U. I. , HUSSAIN, M. , HABIB, Z.
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,374 KB) | Citation | Downloads: 514 | Views: 991|
forensics, image classification, machine learning, multimedia systems
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
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.
Web of Science® Times Cited: 6 [View]
View record in Web of Science® [View]
View Related Records® [View]
SCOPUS® Times Cited: 6
View record in SCOPUS® [Free preview]
View citations in SCOPUS® [Free preview]
 Dual adaptive deep convolutional neural network for video forgery detection in 3D lighting environment, Vinolin, V., Sucharitha, M., The Visual Computer, ISSN 0178-2789, 2020.
Digital Object Identifier: 10.1007/s00371-020-01992-5 [CrossRef]
 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]
 Optical flow and pattern noise-based copy–paste detection in digital videos, Singh, Raahat Devender, Aggarwal, Naveen, Multimedia Systems, ISSN 0942-4962, 2021.
Digital Object Identifier: 10.1007/s00530-020-00749-3 [CrossRef]
 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]
 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]
Disclaimer: All information displayed above was retrieved by using remote connections to respective databases. For the best user experience, we update all data by using background processes, and use caches in order to reduce the load on the servers we retrieve the information from. As we have no control on the availability of the database servers and sometimes the Internet connectivity may be affected, we do not guarantee the information is correct or complete. For the most accurate data, please always consult the database sites directly. Some external links require authentication or an institutional subscription.
Web of Science® is a registered trademark of Clarivate Analytics, Scopus® is a registered trademark of Elsevier B.V., other product names, company names, brand names, trademarks and logos are the property of their respective owners.
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.
Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.
Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.