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.