Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.800
JCR 5-Year IF: 1.000
SCOPUS CiteScore: 2.0
Issues per year: 4
Current issue: Feb 2024
Next issue: May 2024
Avg review time: 78 days
Avg accept to publ: 48 days
APC: 300 EUR


PUBLISHER

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

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


TRAFFIC STATS

2,500,536 unique visits
994,844 downloads
Since November 1, 2009



Robots online now
Googlebot


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

Application of the Voltage Control Technique and MPPT of Stand-alone PV System with Storage, HIVZIEFENDIC, J., VUIC, L., LALE, S., SARIC, M.
Issue 1/2022

AbstractPlus






LATEST NEWS

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

2021-Jun-30
Clarivate Analytics published the InCites Journal Citations Report for 2020. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 1.221 (1.053 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.961.

Read More »


    
 

  3/2019 - 12
View TOC | « Previous Article | Next Article »

 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
 
View the paper record and citations in View the paper record and citations in Google Scholar
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,374 KB) | Citation | Downloads: 1,349 | Views: 3,066

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
Quick view
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

Cited-By Clarivate Web of Science

Web of Science® Times Cited: 17 [View]
View record in Web of Science® [View]
View Related Records® [View]

Updated today


Cited-By SCOPUS

SCOPUS® Times Cited: 23
View record in SCOPUS®
[Free preview]
View citations in SCOPUS® [Free preview]

Updated today

Cited-By CrossRef

[1] Non-Facial Video Spatiotemporal Forensic Analysis Using Deep Learning Techniques, Premanand Ghadekar, , Vaibhavi Shetty, , Prapti Maheshwari, , Raj Shah, , Anish Shaha, , Vaishnav Sonawane,, Proceedings of Engineering and Technology Innovation, ISSN 2518-833X, Issue , 2023.
Digital Object Identifier: 10.46604/peti.2023.10290
[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] A Light Weight Depthwise Separable Layer Optimized CNN Architecture for Object-Based Forgery Detection in Surveillance Videos, Sandhya, , Kashyap, Abhishek, The Computer Journal, ISSN 0010-4620, 2024.
Digital Object Identifier: 10.1093/comjnl/bxae005
[CrossRef]

[4] 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]

[5] 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]

[6] 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]

[7] 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]

[8] 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]

[9] 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]

[10] 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]

[11] 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]

[12] Inter-frame video forgery detection using UFS-MSRC algorithm and LSTM network, Girish, N., Nandini, C., International Journal of Modeling, Simulation, and Scientific Computing, ISSN 1793-9623, Issue 01, Volume 14, 2023.
Digital Object Identifier: 10.1142/S1793962323410131
[CrossRef]

[13] 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]

[14] A Novel Moving Object Detection Algorithm Based on Robust Image Feature Threshold Segmentation with Improved Optical Flow Estimation, Ding, Jing, Zhang, Zhen, Yu, Xuexiang, Zhao, Xingwang, Yan, Zhigang, Applied Sciences, ISSN 2076-3417, Issue 8, Volume 13, 2023.
Digital Object Identifier: 10.3390/app13084854
[CrossRef]

[15] A comprehensive survey on state-of-the-art video forgery detection techniques, Mohiuddin, Sk, Malakar, Samir, Kumar, Munish, Sarkar, Ram, Multimedia Tools and Applications, ISSN 1380-7501, Issue 22, Volume 82, 2023.
Digital Object Identifier: 10.1007/s11042-023-14870-8
[CrossRef]

[16] Image Forgery Detection Using Noise and Edge Weighted Local Texture Features, ASGHAR, K., SADDIQUE, M., HUSSAIN, M., BEBIS, G., HABIB, Z., Advances in Electrical and Computer Engineering, ISSN 1582-7445, Issue 1, Volume 22, 2022.
Digital Object Identifier: 10.4316/AECE.2022.01007
[CrossRef] [Full text]

[17] Duplicate Frame Video Forgery Detection Using Siamese-based RNN, Munawar, Maryam, Noreen, Iram, Intelligent Automation & Soft Computing, ISSN 1079-8587, Issue 3, Volume 29, 2021.
Digital Object Identifier: 10.32604/iasc.2021.018854
[CrossRef]

[18] Object based Forgery Detection in Surveillance Videos using Optimized CNN, Sandhya, , Kashyap, Abhishek, 2022 8th International Conference on Signal Processing and Communication (ICSC), ISBN 978-1-6654-5430-8, 2022.
Digital Object Identifier: 10.1109/ICSC56524.2022.10009279
[CrossRef]

[19] 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]

[20] Digital video tampering detection using texture with compressed passive technic, Vistro, Daniel Mago, Rehman, Attique Ur, Usman, Muhammad, Abbas, Sana, WOMEN IN PHYSICS: 7th IUPAP International Conference on Women in Physics, ISBN , Issue , 2024.
Digital Object Identifier: 10.1063/5.0181757
[CrossRef]

[21] 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]

[22] Combating Online Misinformation Videos: Characterization, Detection, and Future Directions, Bu, Yuyan, Sheng, Qiang, Cao, Juan, Qi, Peng, Wang, Danding, Li, Jintao, Proceedings of the 31st ACM International Conference on Multimedia, ISBN 9798400701085, 2023.
Digital Object Identifier: 10.1145/3581783.3612426
[CrossRef]

Updated today

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.


Copyright ©2001-2024
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.




Website loading speed and performance optimization powered by: 


DNS Made Easy