Video jitter analysis for automatic bootleg detection

This paper presents a novel technique for the automatic detection of recaptured videos with applications to video forensics. The proposed technique uses scene jitter as a cue for classification: when recapturing planar surfaces approximately parallel to the imaging plane, any added motion due to jitter will result in approximately uniform high-frequency 2D motion fields. The inter-frame motion trajectories are retrieved with feature tracking techniques, while local and global feature motion are decoupled through a 2-level wavelet decomposition. A normalised cross-correlation matrix is then populated with the similarities between the high-frequency components of the tracked features' trajectories. The correlation distribution is then compared with trained models for classification. Experiments with original and recaptured standard datasets show the validity of the proposed technique.