  Video stabilization is an in-camera processing commonly applied by modern
acquisition devices. While significantly improving the visual quality of the
resulting videos, it has been shown that such operation typically hinders the
forensic analysis of video signals. In fact, the correct identification of the
acquisition source usually based on Photo Response non-Uniformity (PRNU) is
subject to the estimation of the transformation applied to each frame in the
stabilization phase. A number of techniques have been proposed for dealing with
this problem, which however typically suffer from a high computational burden
due to the grid search in the space of inversion parameters. Our work attempts
to alleviate these shortcomings by exploiting the parallelization capabilities
of Graphics Processing Units (GPUs), typically used for deep learning
applications, in the framework of stabilised frames inversion. Moreover, we
propose to exploit SIFT features {to estimate the camera momentum and} %to
identify less stabilized temporal segments, thus enabling a more accurate
identification analysis, and to efficiently initialize the frame-wise parameter
search of consecutive frames. Experiments on a consolidated benchmark dataset
confirm the effectiveness of the proposed approach in reducing the required
computational time and improving the source identification accuracy. {The code
is available at \url{https://github.com/AMontiB/GPU-PRNU-SIFT}}.
