PRNU Estimation based on Weighted Averaging for Source Smartphone Video Identification

Lawgaly, Ashref, Khelifi, Fouad, Bouridane, Ahmed, Al-Maaddeed, Somaya and Akbari, Younes (2022) PRNU Estimation based on Weighted Averaging for Source Smartphone Video Identification. In: Proceedings of the IEEE International Conference on Control, Decisions and Information Technologies (CODIT 2022). IEEE, Piscataway, US, pp. 1-6. (In Press)

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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.

Item Type: Book Section
Additional Information: Funding information: This work was supported by NPRP grant # NPRP12S-0312- 190332 from the Qatar National Research Fund (a member of the Qatar Foundation).; 8th International Conference on Control, Decision and Information Technologies : CoDIT'22 ; Conference date: 17-05-2022 Through 20-05-2022
Uncontrolled Keywords: PRNU, Source Smartphone Identification, Video Forensics, weighted averaging
Subjects: G600 Software Engineering
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Related URLs:
Depositing User: John Coen
Date Deposited: 16 Mar 2022 11:35
Last Modified: 16 Mar 2022 11:45
URI: http://nrl.northumbria.ac.uk/id/eprint/48681

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