Now-a-days, videos can be easily recorded and
forged with user-friendly editing tools. These videos can be
shared on social networks to make false propaganda. During
the process of spatial forgery, the texture and micro-patterns of
the frames become inconsistent, which can be observed in the
difference of two consecutive frames. Based on this
observation, a method has been proposed for detection of
forged video segments and localization of forged frames.
Employing the Chrominance value of Consecutive frame
Difference (CCD) and Discriminative Robust Local Binary
Pattern (DRLBP), a new descriptor is introduced to model the
inconsistency embedded in the frames due to forgery. Support
Vector Machine (SVM) is used to detect whether the pair of
consecutive frames is forged. If at least one pair of consecutive
frames is detected as forged, the video segment is predicted as
forged and the forged frames are localized. Intensive
experiments are performed to validate the performance of the
method on a combined dataset of videos, which were tampered
by copy-move and splicing methods. The detection accuracy on
large dataset is 96.68 percent and video accuracy is 98.32
percent. The comparison shows that it outperforms the stateof-
the-art methods, even through cross dataset validation.
Keywords: forensics, image classification, machine
learning, multimedia systems.