{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T01:25:43Z","timestamp":1778117143030,"version":"3.51.4"},"reference-count":42,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:00:00Z","timestamp":1614902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Great attention is paid to detecting video forgeries nowadays, especially with the widespread sharing of videos over social media and websites. Many video editing software programs are available and perform well in tampering with video contents or even creating fake videos. Forgery affects video integrity and authenticity and has serious implications. For example, digital videos for security and surveillance purposes are used as evidence in courts. In this paper, a newly developed passive video forgery scheme is introduced and discussed. The developed scheme is based on representing highly correlated video data with a low computational complexity third-order tensor tube-fiber mode. An arbitrary number of core tensors is selected to detect and locate two serious types of forgeries which are: insertion and deletion. These tensor data are orthogonally transformed to achieve more data reductions and to provide good features to trace forgery along the whole video. Experimental results and comparisons show the superiority of the proposed scheme with a precision value of up to 99% in detecting and locating both types of attacks for static as well as dynamic videos, quick-moving foreground items (single or multiple), zooming in and zooming out datasets which are rarely tested by previous works. Moreover, the proposed scheme offers a reduction in time and a linear computational complexity. Based on the used computer\u2019s configurations, an average time of 35 s. is needed to detect and locate 40 forged frames out of 300 frames.<\/jats:p>","DOI":"10.3390\/jimaging7030047","type":"journal-article","created":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T11:46:09Z","timestamp":1614944769000},"page":"47","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Detecting and Locating Passive Video Forgery Based on Low Computational Complexity Third-Order Tensor Representation"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0963-2800","authenticated-orcid":false,"given":"Yasmin M.","family":"Alsakar","sequence":"first","affiliation":[{"name":"Department of Information Technology, Faculty of Computers and Information Science, Mansoura University, Mansoura 35516, Egypt"}]},{"given":"Nagham E.","family":"Mekky","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computers and Information Science, Mansoura University, Mansoura 35516, Egypt"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2334-9523","authenticated-orcid":false,"given":"Noha A.","family":"Hikal","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Faculty of Computers and Information Science, Mansoura University, Mansoura 35516, Egypt"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4548","DOI":"10.1002\/sec.1648","article-title":"Video inter-frame forgery identification based on the consistency of quotient of MSSIM","volume":"9","author":"Li","year":"2016","journal-title":"Secur. Commun. Netw."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sencar, H.T., and Memon, N. (2009). Overview of state-of-the-art in digital image forensics. Algorithms, Architectures and Information Systems Security, World Scientific.","DOI":"10.1142\/9789812836243_0015"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"72347","DOI":"10.1109\/ACCESS.2020.2987870","article-title":"Fast Temporal Video Segmentation Based on Krawtchouk-Tchebichef Moments","volume":"8","author":"Abdulhussain","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","unstructured":"Mehta, V., Jaiswal, A.K., and Srivastava, R. (2013, January 22\u201323). Copy-Move Image Forgery Detection Using DCT and ORB Feature Set. Proceedings of the International Conference on Futuristic Trends in Networks and Computing Technologies, Chandigarh, India."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1109\/TIFS.2010.2074194","article-title":"Detecting forgery from static-scene video based on inconsistency in noise level functions","volume":"5","author":"Kobayashi","year":"2010","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4905","DOI":"10.1007\/s11042-018-6570-8","article-title":"Detection and localization of inter-frame video forgeries based on inconsistency in correlation distribution between Haralick coded frames","volume":"78","author":"Bakas","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.diin.2016.06.003","article-title":"Digital video tampering detection: An overview of passive techniques","volume":"18","author":"Sitara","year":"2016","journal-title":"Digit. Investig."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e1482","DOI":"10.1002\/wics.1482","article-title":"Tensor decomposition for dimension reduction","volume":"12","author":"Cheng","year":"2020","journal-title":"Comput. Stat."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","article-title":"Handcrafted vs. non-handcrafted features for computer vision classification","volume":"71","author":"Nanni","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1007\/s11042-014-2374-7","article-title":"Using similarity analysis to detect frame duplication forgery in videos","volume":"75","author":"Yang","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Singh, V.K., Pant, P., and Tripathi, R.C. (2015, January 6\u20138). Detection of frame duplication type of forgery in digital video using sub-block based features. Proceedings of the International Conference on Digital Forensics and Cyber Crime, Seoul, Korea.","DOI":"10.1007\/978-3-319-25512-5_3"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, H., Li, S., and Bian, S. (2014, January 5\u20138). Detecting frame deletion in H. 264 video. Proceedings of the International Conference on Information Security Practice and Experience, Fuzhou, China.","DOI":"10.1007\/978-3-319-06320-1_20"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.neucom.2016.03.051","article-title":"Exposing frame deletion by detecting abrupt changes in video streams","volume":"205","author":"Yu","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"51","DOI":"10.4236\/jcc.2014.24008","article-title":"Video inter-frame forgery identification based on consistency of correlation coefficients of gray values","volume":"2","author":"Wang","year":"2014","journal-title":"J. Comput. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1002\/sec.981","article-title":"Efficient video frame insertion and deletion detection based on inconsistency of correlations between local binary pattern coded frames","volume":"8","author":"Zhang","year":"2015","journal-title":"Secur. Commun. Netw."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.image.2016.07.001","article-title":"Inter-frame video forgery detection and localization using intrinsic effects of double compression on quantization errors of video coding","volume":"47","author":"Aghamaleki","year":"2016","journal-title":"Signal Process. Image Commun."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"25389","DOI":"10.1007\/s11042-018-5791-1","article-title":"Inter-frame passive-blind forgery detection for video shot based on similarity analysis","volume":"77","author":"Zhao","year":"2018","journal-title":"Multimed. Tools Appl."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1365","DOI":"10.1007\/s11045-020-00711-6","article-title":"Exposing video inter-frame forgery via histogram of oriented gradients and motion energy image","volume":"31","author":"Fadl","year":"2020","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_19","unstructured":"Long, C., Basharat, A., and Hoogs, A. (2021, February 10). A Coarse-to-fine Deep Convolutional Neural Network Framework for Frame Duplication Detection and Localization in Video Forgery. CVPR Workshops 2019. Available online: http:\/\/www.chengjianglong.com\/publications\/CopyPaste.pdf."},{"key":"ref_20","unstructured":"Bakas, J., and Naskar, R. (2014, January 17\u201319). A Digital Forensic Technique for Inter\u2013Frame Video Forgery Detection Based on 3D CNN. Proceedings of the International Conference on Information Systems Security, Bangalore, India."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, Q., Wang, R., and Xu, D. (2018). An Inter-Frame Forgery Detection Algorithm for Surveillance Video. Information, 9.","DOI":"10.3390\/info9120301"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Subramanyam, A.V., and Emmanuel, S. (2013, January 26\u201331). Pixel estimation based video forgery detection. Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638216"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Huang, Z., Huang, F., and Huang, J. (2014, January 9\u201313). Detection of double compression with the same bit rate in MPEG-2 videos. Proceedings of the 2014 IEEE China Summit & International Conference on Signal and Information Processing (ChinaSIP), Xi\u2019an, China.","DOI":"10.1109\/ChinaSIP.2014.6889253"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2138","DOI":"10.1109\/TCSVT.2015.2473436","article-title":"Automatic detection of object-based forgery in advanced video","volume":"26","author":"Chen","year":"2015","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"D\u2019Amiano, L., Cozzolino, D., Poggi, G., and Verdoliva, L. (July, January 29). Video forgery detection and localization based on 3D patchmatch. Proceedings of the 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Torino, Italy.","DOI":"10.1109\/ICMEW.2015.7169805"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Bidokhti, A., and Ghaemmaghami, S. (2015, January 3\u20135). Detection of regional copy\/move forgery in MPEG videos using optical flow. Proceedings of the 2015 The International Symposium on Artificial Intelligence and Signal Processing (AISP), Mashhad, Iran.","DOI":"10.1109\/AISP.2015.7123529"},{"key":"ref_27","unstructured":"Schwenker, F., and Scherer, S. (2017). Face Recognition in Home Security System Using Tensor Decomposition Based on Radix-(2 \u00d7 2) Hierarchical SVD. Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction, Springer International Publishing."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e1314","DOI":"10.1002\/widm.1314","article-title":"Hierarchical third-order tensor decomposition through inverse difference pyramid based on the three-dimensional Walsh\u2013Hadamard transform with app.lications in data mining","volume":"10","author":"Kountchev","year":"2020","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kountchev, R.K., Mironov, R.P., and Kountcheva, R.A. (2020). Hierarchical Cubical Tensor Decomposition through Low Complexity Orthogonal Transforms. Symmetry, 12.","DOI":"10.3390\/sym12050864"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Kountchev, R., and Kountcheva, R. (2020). Low Computational Complexity Third-Order Tensor Representation Through Inverse Spectrum Pyramid. Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology, Springer.","DOI":"10.1007\/978-981-15-3863-6_8"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s10851-018-0863-4","article-title":"A new hybrid form of krawtchouk and tchebichef polynomials: Design and application","volume":"61","author":"Abdulhussain","year":"2019","journal-title":"J. Math. Imaging Vis."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Mahmmod, B.M., Abdul-Hadi, A.M., Abdulhussain, S.H., and Hussien, A.J. (2020). On computational aspects of Krawtchouk polynomials for high orders. J. Imaging, 6.","DOI":"10.3390\/jimaging6080081"},{"key":"ref_33","first-page":"9","article-title":"Automated forensic method for copy-move forgery detection based on Harris interest points and SIFT descriptors","volume":"27","author":"Shivakumar","year":"2011","journal-title":"Int. J. Comput. Appl."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.jvcir.2013.01.008","article-title":"Region duplication detection based on Harris corner points and step sector statistics","volume":"24","author":"Chen","year":"2013","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1137\/0713009","article-title":"Generalizing the singular value decomposition","volume":"13","year":"1976","journal-title":"J. Numer. Anal."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e4483","DOI":"10.1136\/bmj.e4483","article-title":"Pearson\u2019s correlation coefficient","volume":"345","author":"Sedgwick","year":"2012","journal-title":"BMJ"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Amidan, B.G., Ferryman, T.A., and Cooley, S.K. (2005, January 5\u201312). Data outlier detection using the Chebyshev theorem. Proceedings of the 2005 IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2005.1559688"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1109\/TBC.2013.2244792","article-title":"Traffic and statistical multiplexing characterization of 3-D video representation formats","volume":"59","author":"Pulipaka","year":"2013","journal-title":"IEEE Trans. Broadcasting"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Su, Y., Nie, W., and Zhang, C. (2015, January 20\u201322). A frame tampering detection algorithm for MPEG videos. Proceedings of the 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, Chongqing, China.","DOI":"10.1109\/ITAIC.2011.6030373"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1504\/IJESDF.2017.085196","article-title":"A review of video falsifying techniques and video forgery detection techniques","volume":"9","author":"Mizher","year":"2017","journal-title":"Int. J. Electron. Secur. Digit. Forensics"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.diin.2013.10.004","article-title":"Detection of frame deletion for digital video forensics","volume":"10","author":"Shanableh","year":"2013","journal-title":"Digit. Investig."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s00530-015-0478-1","article-title":"Exposing video inter-frame forgery by Zernike opponent chromaticity moments and coarseness analysis","volume":"23","author":"Liu","year":"2017","journal-title":"Multimed. Syst."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/3\/47\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:33:43Z","timestamp":1760160823000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/3\/47"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,5]]},"references-count":42,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["jimaging7030047"],"URL":"https:\/\/doi.org\/10.3390\/jimaging7030047","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,5]]}}}