A New Forensic Video Database for Source Smartphone Identification: Description and Analysis

Akbari, Younes, Al-Maadeed, Somaya, Al-Maadeed, Noor, Najeeb, Al Anood, Al-Ali, Afnan, Khelifi, Fouad and Lawgaly, Ashref (2022) A New Forensic Video Database for Source Smartphone Identification: Description and Analysis. IEEE Access, 10. pp. 20080-20091. ISSN 2169-3536

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Official URL: https://doi.org/10.1109/ACCESS.2022.3151406


In recent years, the field of digital imaging has made significant progress, so that today every smartphone has a built-in video camera that allows you to record high-quality video for free and without restrictions. On the other hand, rapidly growing internet technology has contributed significantly to the widespread use of digital video via web-based multimedia systems and mobile smartphone applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital videos have become affordable nowadays, security issues have become threatening and spread worldwide. One of the security issues is identifying source cameras on videos. There are some new challenges that should be addressed in this area. One of the new challenges is individual source camera identification (ISCI), which focuses on identifying each device regardless of its model. The first step towards solving the problems is a popular video database recorded by modern smartphone devices, which can also be used for deep learning methods that are growing rapidly in the field of source camera identification. In this paper, a smartphone video database named Qatar University Forensic Video Database (QUFVD) is introduced. The QUFVD includes 6000 videos from 20 modern smartphone representing five brands, each brand has two models, and each model has two identical smartphone devices. This database is suitable for evaluating different techniques such as deep learning methods for video source smartphone identification and verification. To evaluate the QUFVD, a series of experiments to identify source cameras using a deep learning technique are conducted. The results show that improvements are essential for the ISCI scenario on video.

Item Type: Article
Additional Information: Funding information: This publication was made possible by NPRP grant # NPRP12S-0312-190332 from Qatar National Research Fund (a member of Qatar Foundation). Open Access funding provided by the Qatar National Library.
Uncontrolled Keywords: Database, smart phone, source camera identification on videos, deep learning methods
Subjects: G500 Information Systems
J900 Others in Technology
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 01 Mar 2022 11:58
Last Modified: 01 Mar 2022 12:00
URI: http://nrl.northumbria.ac.uk/id/eprint/48576

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