The ubiquitous and connected nature of camera loaded mobile devices has greatly estimated the value and importance of visual information they capture. Today, sending videos from camera phones uploaded by unknown users is relevant on news networks, and banking customers expect to be able to deposit checks using mobile devices. In this paper we represent Movee, a system that addresses the fundamental question of whether the visual stream exchange by a user has been captured live on a mobile device, and has not been tampered with by an adversary. Movee leverages the mobile device motion sensors and the inherent user movements during the shooting of the video. Movee exploits the observation that the movement of the scene recorded on the video stream should be related to the movement of the device simultaneously captured by the accelerometer. the last decade e-lecturing has become more and more popular. We model the distribution of correlation of temporal noise residue in a forged video as a Gaussian mixture model (GMM). We propose a twostep scheme to estimate the model parameters. Consequently, a Bayesian classifier is used to find the optimal threshold value based on the estimated parameters.
Lecture videos, automatic video indexing, content-based video search, lecture video archives
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