A New Forensic Video Database for Source Smartphone Identification: Description and Analysis
Author | Akbari, Y. |
Author | Al-Maadeed, Somaya |
Author | Almaadeed, N. |
Author | Najeeb, A.A. |
Author | Al-ali, A. |
Author | Khelifi, F. |
Author | Lawgaly, A. |
Available date | 2022-05-19T10:23:06Z |
Publication Date | 2022 |
Publication Name | IEEE Access |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1109/ACCESS.2022.3151406 |
Abstract | 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. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | Computer 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) |
Type | Article |
Files in this item
Files | Size | Format | View |
---|---|---|---|
There are no files associated with this item. |
This item appears in the following Collection(s)
-
Computer Science & Engineering [2281 items ]