PRNU Estimation based on Weighted Averaging for Source Smartphone Video Identification

Abstract

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing imperfections in the imaging device. The PRNU is a unique noise for each sensor device, and it has been generally utilized in the literature for source camera identification and image authentication. In video forensics, the traditional approach estimates the PRNU by averaging a set of residual signals obtained from multiple video frames. However, due to lossy compression and other non-unique content-dependent noise components that interfere with the video data, constant averaging does not take into account the intensity of these undesirable noise components which are content-dependent. Different from the traditional approach, we propose a video PRNU estimation method based on weighted averaging. The noise residual is first extracted for each single video. Then, the estimated noise residuals are fed into a weighted averaging method to optimize PRNU estimation. Experimental results on two video datasets captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-the-art one.