Digital Forensic Analysis for Source Video Identification: a Survey

Akbari, Younes, Almaadeed, Somaya, Elharrouss, Omar, Khelifi, Fouad, Lawgaly, Ashref and Bouridane, Ahmed (2022) Digital Forensic Analysis for Source Video Identification: a Survey. Forensic Science International, 41. p. 301390. ISSN 0379-0738

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Official URL: https://doi.org/10.1016/j.fsidi.2022.301390

Abstract

In recent years, many digital devices have been equipped with a video camera that allows videos to be recorded in good quality, free of charge and without restrictions. Concurrently, the widespread use of digital videos via web-based multimedia systems and mobile smartphone applications such as YouTube, Facebook, Twitter and WhatsApp is becoming increasing important. However, security challenges have emerged and are spreading worldwide. These issues may lead to serious problems, particularly in situations where video is a key part of decision-making in crimes, including movie piracy and child pornography. Thus, to increase the trustworthiness of using digital video in daily life, copyright protection and video authentication must be used. Although source camera identification based on digital images has attracted many researchers’ attention, less research has been performed on the forensic analysis of videos due to certain challenges, such as compression, stabilization, scaling, and cropping, as well as differences between frame types that can occur when a video is stored in digital devices. Thus, there are insufficient large standard digital video databases and updated databases with new devices based on new technologies. The goal of this paper is to offer an inclusive overview of what has been done over the last decade in the field of source video identification by examining existing techniques, such as photo response nonuniformity (PRNU) and machine learning approaches, and describing some popular video databases.

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). The statement made herein are solely the responsibility of the authors.
Uncontrolled Keywords: Survey, Source camera identification, Video, PRNU, Machine learning methods
Subjects: F400 Forensic and Archaeological Science
G400 Computer Science
G700 Artificial Intelligence
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 06 May 2022 10:13
Last Modified: 16 May 2022 08:30
URI: http://nrl.northumbria.ac.uk/id/eprint/49059

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