Video Forgery Detection Using a Bayesian RJMCMC-Based Approach

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.