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Video Forgery Detection Using a Bayesian RJMCMC-Based Approach
Conference proceeding

Video Forgery Detection Using a Bayesian RJMCMC-Based Approach

Sami Bourouis, F. R. Al-Osaimi, Nizar Bouguila, Hassen Sallay, Fahd Aldosari, Mohamed Al Mashrgy and IEEE
2017 IEEE/ACS 14th International Conference on Computer Systems and Applications (AICCSA), Vol.2017-, pp.71-75
10/2017
DOI: https://doi.org/10.1109/AICCSA.2017.43

Abstract

Bayes methods Correlation Data models Electronic mail Feature extraction Forgery Mixture models
We propose a Bayesian approach to learn finite generalized inverted Dirichlet mixture models. The developed approach performs simultaneous parameters estimation, model complexity determination, and feature selection via a reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm. A challenging application that concerns video forgery detection is deployed to validate our statistical framework and to show its merits.

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