A New Forensic Video Database for Source Smartphone Identification: Description and Analysis
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...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9713852/ |
| _version_ | 1819005980057272320 |
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| author | Younes Akbari Somaya Al-Maadeed Noor Al-Maadeed Al Anood Najeeb Afnan Al-Ali Fouad Khelifi Ashref Lawgaly |
| author_facet | Younes Akbari Somaya Al-Maadeed Noor Al-Maadeed Al Anood Najeeb Afnan Al-Ali Fouad Khelifi Ashref Lawgaly |
| author_sort | Younes Akbari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T00:01:24Z |
| format | Article |
| id | doaj.art-76bd4dfa55bc40b7acbaf44a3dad50ab |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-21T00:01:24Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-76bd4dfa55bc40b7acbaf44a3dad50ab2022-12-21T19:22:35ZengIEEEIEEE Access2169-35362022-01-0110200802009110.1109/ACCESS.2022.31514069713852A New Forensic Video Database for Source Smartphone Identification: Description and AnalysisYounes Akbari0https://orcid.org/0000-0001-7175-4326Somaya Al-Maadeed1https://orcid.org/0000-0002-0241-2899Noor Al-Maadeed2Al Anood Najeeb3https://orcid.org/0000-0001-8764-4201Afnan Al-Ali4https://orcid.org/0000-0002-8307-6806Fouad Khelifi5https://orcid.org/0000-0001-7413-0025Ashref Lawgaly6https://orcid.org/0000-0001-6715-1645Department of Computer Science and Engineering, Qatar University, Doha, QatarDepartment of Computer Science and Engineering, Qatar University, Doha, QatarDepartment of Computer Science and Engineering, Qatar University, Doha, QatarDepartment of Computer Science and Engineering, Qatar University, Doha, QatarDepartment of Computer Science and Engineering, Qatar University, Doha, QatarDepartment of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K.Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne, U.K.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.https://ieeexplore.ieee.org/document/9713852/Databasesmart phonesource camera identification on videosdeep learning methods |
| spellingShingle | Younes Akbari Somaya Al-Maadeed Noor Al-Maadeed Al Anood Najeeb Afnan Al-Ali Fouad Khelifi Ashref Lawgaly A New Forensic Video Database for Source Smartphone Identification: Description and Analysis IEEE Access Database smart phone source camera identification on videos deep learning methods |
| title | A New Forensic Video Database for Source Smartphone Identification: Description and Analysis |
| title_full | A New Forensic Video Database for Source Smartphone Identification: Description and Analysis |
| title_fullStr | A New Forensic Video Database for Source Smartphone Identification: Description and Analysis |
| title_full_unstemmed | A New Forensic Video Database for Source Smartphone Identification: Description and Analysis |
| title_short | A New Forensic Video Database for Source Smartphone Identification: Description and Analysis |
| title_sort | new forensic video database for source smartphone identification description and analysis |
| topic | Database smart phone source camera identification on videos deep learning methods |
| url | https://ieeexplore.ieee.org/document/9713852/ |
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