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Volume: 31 | Article ID: art00017
Statistical Sequential Analysis for Object-based Video Forgery Detection
  DOI :  10.2352/ISSN.2470-1173.2019.5.MWSF-543  Published OnlineJanuary 2019

Over the years, video surveillance systems have been used for indisputable evidence of a crime. Unfortunately, videos of the surveillance systems can be forged through adding (deleting) an object to (from) a video scene (i.e., object-based forgery) with invisible traces and little effort. In this paper, we propose a novel approach that uses spatial decomposition, temporal filtering, and sequential analysis to detect object-based video forgery and estimate a movement of removed objects. The results show that our approach not only outperforms a previous approach in detecting forged videos but it is also more robust against compressed and lower resolution videos. Also, our approach can effectively estimate a movement of different sizes of removed objects.

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Mohammed Aloraini, Mehdi Sharifzadeh, Chirag Agarwal, Dan Schonfeld, "Statistical Sequential Analysis for Object-based Video Forgery Detectionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Media Watermarking, Security, and Forensics,  2019,  pp 543-1 - 543-7,

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