Novel Blind Video Forgery Detection Using Markov Models on Motion Residue

  • Kesav Kancherla
  • Srinivas Mukkamala
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7198)

Abstract

In this paper we present a novel blind video forgery detection method by applying Markov models to motion in videos. Motion is an important aspect of video forgery detection as it effects forgery detection in videos. Most of the current video forgery detection algorithms do not consider motion in their approach. Motion is usually captured from motion vectors and prediction error frame. However capturing motion for I-frame is computationally expensive, so in this paper we extract the motion information by applying collusion on successive frames. First a base frame is obtained by applying collusion on successive frames and the difference between actual and estimate gives information about motion. Then we apply Markov models on this motion residue and apply pattern recognition on this. We used Support Vector Machines (SVMs) in our experiment. We obtained an accuracy of 87% even for reduced feature set.

Keywords

Motion Vector Base Frame Forgery Attack Motion Picture Expert Group Apply Pattern Recognition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kesav Kancherla
    • 1
  • Srinivas Mukkamala
    • 1
  1. 1.Department of Computer Science Institute for Complex Additive Systems and Analysis (ICASA) Computational Analysis and Network Enterprise Solutuons (CAaNES)New Mexico Institute of Mining and TechnologySocorroU.S.A.

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