  A problem deeply investigated by multimedia forensics researchers is the one
of detecting which device has been used to capture a video. This enables to
trace down the owner of a video sequence, which proves extremely helpful to
solve copyright infringement cases as well as to fight distribution of illicit
material (e.g., underage clips, terroristic threats, etc.). Currently, the most
promising methods to tackle this task exploit unique noise traces left by
camera sensors on acquired images. However, given the recent advancements in
motion stabilization of video content, robustness of sensor pattern noise-based
techniques are strongly hindered. Indeed, video stabilization introduces
geometric transformations between video frames, thus making camera fingerprint
estimation problematic with classical approaches. In this paper, we deal with
the challenging problem of attributing stabilized videos to their recording
device. Specifically, we propose: (i) a strategy to extract the characteristic
fingerprint of a device, starting from either a set of images or stabilized
video sequences; (ii) a strategy to match a stabilized video sequence with a
given fingerprint in order to solve the device attribution problem. The
proposed methodology is tested on videos coming from a set of different
smartphones, taken from the modern publicly available Vision Dataset. The
conducted experiments also provide an interesting insight on the effect of
modern smartphones video stabilization algorithms on specific video frames.
