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AuthorAkbari, Y.
AuthorAl-Maadeed, Somaya
AuthorAlmaadeed, N.
AuthorNajeeb, A.A.
AuthorAl-ali, A.
AuthorKhelifi, F.
AuthorLawgaly, A.
Available date2022-05-19T10:23:06Z
Publication Date2022
Publication NameIEEE Access
ResourceScopus
Identifierhttp://dx.doi.org/10.1109/ACCESS.2022.3151406
URIhttp://hdl.handle.net/10576/31078
AbstractIn 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.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectComputer graphics
Database systems
Deep learning
Digital forensics
E-learning
Face recognition
Multimedia systems
Object recognition
Quality control
Smartphones
Video cameras
Deep learning
Deep learning method
Forensic
Learning methods
Objects recognition
Smart phones
Social networking (online)
Source camera identification on video
Source camera identifications
Video database
Social networking (online)
TitleA New Forensic Video Database for Source Smartphone Identification: Description and Analysis
TypeArticle
dc.accessType Abstract Only


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