A Tool for Video Forgery Detection in Video Sequences
Main Article Content
Abstract
Because of the vast development of the video editing tools, the digital videos are playing main role in todays’ context. Because of the variety of techniques available, it is very easy to alter and edit original content of a video. So paper describes about implemented system for video surveillance which could apply for tamper detection. As an inputs system is getting the videos which are needed to be tested. System could use, for law enforcements, CCTV (Closed-circuit television) videos, for news broadcasting. Mainly this system is considering developing a system for analysing shadow variation with the noise and system recognize an object and extract shadow from the object and for detect moving objects and track objects. After this approach system will identify pattern variations in shadow, motions and create a model by using SimpleKMeans approach as a clustering technique in data mining and frame removed edited videos by using Naïve Bayes classification algorithm in data mining. According to the pre identified model, user input videos will be identified as tampered or not. The model creation was done for 3 CCTV s. From each CCTV system has taken 20 videos .And implemented the model for 5 noise levels, kernel size11, 15,25,55,75 for shadow detection and frames were removed in-between original videos for different time levels. According to model, input video from predefined CCTV gives noise level of the video and error rate of each noise level .And objects are tracked and according to motion vector, the variance of the objects will be calculated. This result is used in data mining and according to that prediction happens.
Keywords: SimpleKMeans, Naïve Bayes, Data Mining, Motion Vector, CCTV, Weka
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.