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}}.
翻译:视频稳定化是一种由现代购置装置常用的相机处理程序。 虽然这种操作极大地提高了所产生视频的视觉质量,但已经表明,这种操作通常会妨碍对视频信号的法证分析。事实上,对通常以照片反应不统一(PRNU)为基础的获取源的正确识别取决于对稳定化阶段每个框架所应用的变异的估计。已经提出一些技术来处理这一问题,但是由于在反向参数空间进行网格搜索,这一问题通常会受到高计算负担的影响。我们试图通过利用图形处理器(GPUs)的平行能力来减轻这些缺陷,这些能力通常用于在稳定化框架翻版框架内的深层学习应用。此外,我们提议利用SIMFT的特性{来估计摄像力和}%来识别不稳定的时段,从而能够进行更准确的识别分析,并高效地启动对连续框架进行的框架偏向参数搜索。在综合基准数据集上进行的实验证实了拟议方法在减少所需计算时间和提高源码准确性方面的有效性。{http://GMUG_B_FTUG/COI/G。