  We introduce a new method for camera-model identification. Our approach
combines two independent aspects of video file generation corresponding to
video coding and media data encapsulation. To this end, a joint representation
of the overall file metadata is developed and used in conjunction with a
two-level hierarchical classification method. At the first level, our method
groups videos into metaclasses considering several abstractions that represent
high-level structural properties of file metadata. This is followed by a more
nuanced classification of classes that comprise each metaclass. The method is
evaluated on more than 20K videos obtained by combining four public video
datasets. Tests show that a balanced accuracy of 91% is achieved in correctly
identifying the class of a video among 119 video classes. This corresponds to
an improvement of 6.5% over the conventional approach based on video file
encapsulation characteristics. Furthermore, we investigate a setting relevant
to forensic file recovery operations where file metadata cannot be located or
are missing but video data is partially available. By estimating a partial list
of encoding parameters from coded video data, we demonstrate that an
identification accuracy of 57% can be achieved in camera-model identification
in the absence of any other file metadata.
