3 research outputs found
Source Camera Verification from Strongly Stabilized Videos
Image stabilization performed during imaging and/or post-processing poses one
of the most significant challenges to photo-response non-uniformity based
source camera attribution from videos. When performed digitally, stabilization
involves cropping, warping, and inpainting of video frames to eliminate
unwanted camera motion. Hence, successful attribution requires the inversion of
these transformations in a blind manner. To address this challenge, we
introduce a source camera verification method for videos that takes into
account the spatially variant nature of stabilization transformations and
assumes a larger degree of freedom in their search. Our method identifies
transformations at a sub-frame level, incorporates a number of constraints to
validate their correctness, and offers computational flexibility in the search
for the correct transformation. The method also adopts a holistic approach in
countering disruptive effects of other video generation steps, such as video
coding and downsizing, for more reliable attribution. Tests performed on one
public and two custom datasets show that the proposed method is able to verify
the source of 23-30% of all videos that underwent stronger stabilization,
depending on computation load, without a significant impact on false
attribution
Video Source Characterization Using Encoding and Encapsulation Characteristics
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