{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T16:16:11Z","timestamp":1761581771494,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T00:00:00Z","timestamp":1543363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1736215, 61672302, 61771270"],"award-info":[{"award-number":["U1736215, 61672302, 61771270"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Zhejiang Natural Science Foundation","award":["LZ15F020010, Y17F020051, LY17F020013"],"award-info":[{"award-number":["LZ15F020010, Y17F020051, LY17F020013"]}]},{"DOI":"10.13039\/100007834","name":"Ningbo Natural Science Foundation","doi-asserted-by":"publisher","award":["2017A610123"],"award-info":[{"award-number":["2017A610123"]}],"id":[{"id":"10.13039\/100007834","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ningbo University Fund","award":["XKXL1509, XKXL1503"],"award-info":[{"award-number":["XKXL1509, XKXL1503"]}]},{"name":"Mobile Network Application Technology Key Laboratory of Zhejiang Province","award":["F2018001"],"award-info":[{"award-number":["F2018001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Surveillance systems are ubiquitous in our lives, and surveillance videos are often used as significant evidence for judicial forensics. However, the authenticity of surveillance videos is difficult to guarantee. Ascertaining the authenticity of surveillance video is an urgent problem. Inter-frame forgery is one of the most common ways for video tampering. The forgery will reduce the correlation between adjacent frames at tampering position. Therefore, the correlation can be used to detect tamper operation. The algorithm is composed of feature extraction and abnormal point localization. During feature extraction, we extract the 2-D phase congruency of each frame, since it is a good image characteristic. Then calculate the correlation between the adjacent frames. In the second phase, the abnormal points were detected by using k-means clustering algorithm. The normal and abnormal points were clustered into two categories. Experimental results demonstrate that the scheme has high detection and localization accuracy.<\/jats:p>","DOI":"10.3390\/info9120301","type":"journal-article","created":{"date-parts":[[2018,11,28]],"date-time":"2018-11-28T11:43:44Z","timestamp":1543405424000},"page":"301","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["An Inter-Frame Forgery Detection Algorithm for Surveillance Video"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4619-3920","authenticated-orcid":false,"given":"Qian","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Engineering, Ningbo Dahongying University, Ningbo 315175, China"},{"name":"CKC Software Laboratory, Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rangding","family":"Wang","sequence":"additional","affiliation":[{"name":"CKC Software Laboratory, Ningbo University, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawen","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Ningbo University of Technology, Ningbo 315211, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Katsaounidou, A., Dimoulas, C., and Veglis, A. (2018). Cross-Media Authentication and Verification: Emerging Research and Opportunities, IGI Global.","DOI":"10.4018\/978-1-5225-5592-6"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10855","DOI":"10.1007\/s11042-015-2800-5","article-title":"A robust video watermarking technique for the tamper detection of surveillance systems","volume":"75","author":"Arab","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/TIFS.2014.2362848","article-title":"Live video forensics: Source identification in lossy wireless networks","volume":"10","author":"Chen","year":"2015","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.image.2017.04.009","article-title":"Dealing with video source identification in social networks","volume":"57","author":"Amerini","year":"2017","journal-title":"Signal Process. Image Commun."},{"key":"ref_5","first-page":"102441C","article-title":"Review of passive-blind detection in digital video forgery based on sensing and imaging techniques","volume":"10244","author":"Tao","year":"2017","journal-title":"Proc. SPIE"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Li, Z.H., Jia, R.S., Zhang, Z.Z., Liang, X.Y., and Wang, J.W. (2017, January 26\u201328). Double HEVC compression detection with different bitrates based on co-occurrence matrix of PU types and DCT coefficients. Proceedings of the ITM Web of Conferences, Guangzhou, China.","DOI":"10.1051\/itmconf\/20171201020"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.jvcir.2015.11.014","article-title":"Double compression detection based on local motion vector field analysis in static-background videos","volume":"35","author":"He","year":"2016","journal-title":"J. Vis. Commun. Image R"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zheng, J., Sun, T., Jiang, X., and He, P. (2017). Double H.264 compression detection scheme based on prediction residual of background regions. Intelligent Computing Theories and Application, Springer.","DOI":"10.1007\/978-3-319-63309-1_43"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, L., Wang, X., Zhang, W., Yang, G., and Hu, G. (2013, January 1\u20134). Detecting removed object from video with stationary background. Proceedings of the International Workshop on Digital Forensics and Watermarking, Auckland, New Zealand.","DOI":"10.1007\/978-3-642-40099-5_20"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.diin.2014.03.016","article-title":"A passive approach for effective detection and localization of region-level video forgery with spatio-temporal coherence analysis","volume":"11","author":"Lin","year":"2014","journal-title":"Digit. Investig."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/j.forsciint.2013.12.022","article-title":"Detection of object-based manipulation by the statistical features of object contour","volume":"236","author":"Chen","year":"2014","journal-title":"Forensic Sci. Int."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6641","DOI":"10.1007\/s11042-014-1915-4","article-title":"A video forgery detection algorithm based on compressive sensing","volume":"74","author":"Su","year":"2015","journal-title":"Multimed. Tools Appl."},{"key":"ref_13","first-page":"199","article-title":"Review of techniques for the detection of passive video forgeries","volume":"2","author":"Mulla","year":"2017","journal-title":"Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol."},{"key":"ref_14","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_15","first-page":"215","article-title":"Design and performanc optimization of surveillance video Inter-frame forgery detection system","volume":"51","author":"Han","year":"2018","journal-title":"Commun. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Su, Y.T., Ning, W.Z., and Zhang, C.Q. (2011, January 20\u201322). A frame tampering detection algorithm for MPEG videos. Proceedings of the IEEE Joint International Information Technology and Artificial Intelligence Conference, Chongqing, China.","DOI":"10.1109\/ITAIC.2011.6030373"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.diin.2012.07.002","article-title":"A MCEA based passive forensics scheme for detecting frame-based video tampering","volume":"9","author":"Dong","year":"2012","journal-title":"Digit. Investig."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.1109\/TCSVT.2016.2593612","article-title":"Motion-adaptive frame deletion detection for digital video forensics","volume":"27","author":"Feng","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"13833","DOI":"10.1007\/s11042-015-2762-7","article-title":"Forensics and counter anti-forensics of video inter-frame forgery","volume":"75","author":"Kang","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chao, J., Jiang, X.H., and Sun, T.F. (2013). A novel video Inter-frame forgery model detection scheme based on optical flow consistency. Digital Forensics and Watermaking, Springer.","DOI":"10.1007\/978-3-642-40099-5_22"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Wu, Y., Jiang, X., Sun, T., and Wang, W. (2014, January 4\u20139). Exposing video inter-frame forgery based on velocity field consistency. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Florence, Italy.","DOI":"10.1109\/ICASSP.2014.6854085"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1007\/978-3-319-31960-5_9","article-title":"Inter-frame forgery detection for static-background video based on MVP consistency","volume":"9569","author":"Zhang","year":"2016","journal-title":"Proc. Lect. Notes Comput. Sci."},{"key":"ref_24","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_25","first-page":"42","article-title":"Video tamper detection method based on nonnegative tensor factorization","volume":"3","author":"Zhang","year":"2017","journal-title":"Chin. J. Netw. Inf. Secur."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Pang, T., Liang, X., and Li, Z. (2017, January 16\u201318). Frame-deletion detection for static-background video based on multi-scale mutual information. Proceedings of the International Conference on Cloud Computing and Security (ICCCS), Nanjing, China.","DOI":"10.1007\/978-3-319-68542-7_31"},{"key":"ref_27","first-page":"65050","article-title":"Image splicing detection using 2-D phase congruency and statistical moments of characteristic function","volume":"6505","author":"Chen","year":"2007","journal-title":"Proc. SPIE"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1038\/324250a0","article-title":"Mach bands are phase dependent","volume":"324","author":"Morrone","year":"1986","journal-title":"Nature"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1098\/rspb.1988.0073","article-title":"Feature detection in human vision: A phase-dependent energy model","volume":"235","author":"Morrone","year":"1988","journal-title":"Proc. R. Soc. Lond. B"},{"key":"ref_30","first-page":"1","article-title":"Image features from phase congruency","volume":"1","author":"Kovesi","year":"1999","journal-title":"J. Comput. Vis. Res."},{"key":"ref_31","first-page":"493","article-title":"Digital video forgeries detection based on content continuity","volume":"47","author":"Huang","year":"2011","journal-title":"J. Nanjing Univ. (Nat. Sci.)"},{"key":"ref_32","unstructured":"(2018, November 22). k-Means Clustering. Available online: https:\/\/en.wikipedia.org\/wiki\/K-means_clustering#cite_note-lloyd1957-3."},{"key":"ref_33","first-page":"32","article-title":"Recognizing human actions: A local SVM approach","volume":"33","author":"Schuldt","year":"2004","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Qadir, G., Yahaya, S., and Ho, A.T.S. (2012, January 3\u20134). Surrey university library for forensic analysis (SULFA) of video content. Proceedings of the IET Conference on Image Processing, London, UK.","DOI":"10.1049\/cp.2012.0422"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/12\/301\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:32:50Z","timestamp":1760196770000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/9\/12\/301"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,28]]},"references-count":34,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["info9120301"],"URL":"https:\/\/doi.org\/10.3390\/info9120301","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2018,11,28]]}}}