A New Forensic Video Database for Source Smartphone Identification: Description and Analysis
<p>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 sign...
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2022
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| _version_ | 1864513561035800576 |
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| author | Younes Akbari (16303286) |
| author2 | Somaya Al-Maadeed (5178131) Noor Al-Maadeed (16864251) Al Anood Najeeb (16904775) Afnan Al-Ali (16888695) Fouad Khelifi (16904778) Ashref Lawgaly (16904781) |
| author2_role | author author author author author author |
| author_facet | Younes Akbari (16303286) Somaya Al-Maadeed (5178131) Noor Al-Maadeed (16864251) Al Anood Najeeb (16904775) Afnan Al-Ali (16888695) Fouad Khelifi (16904778) Ashref Lawgaly (16904781) |
| author_role | author |
| dc.creator.none.fl_str_mv | Younes Akbari (16303286) Somaya Al-Maadeed (5178131) Noor Al-Maadeed (16864251) Al Anood Najeeb (16904775) Afnan Al-Ali (16888695) Fouad Khelifi (16904778) Ashref Lawgaly (16904781) |
| dc.date.none.fl_str_mv | 2022-02-14T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2022.3151406 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/A_New_Forensic_Video_Database_for_Source_Smartphone_Identification_Description_and_Analysis/24056388 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electronics, sensors and digital hardware Information and computing sciences Computer vision and multimedia computation Cybersecurity and privacy Data management and data science Machine learning Databases Cameras Forensics Deep learning Training Social networking (online) Object recognition Smart phone Source camera identification on videos Deep learning methods |
| dc.title.none.fl_str_mv | A New Forensic Video Database for Source Smartphone Identification: Description and Analysis |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3151406" target="_blank">https://dx.doi.org/10.1109/access.2022.3151406</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_7fcf0e21760f2f5c599902636e2714e9 |
| identifier_str_mv | 10.1109/access.2022.3151406 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24056388 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | A New Forensic Video Database for Source Smartphone Identification: Description and AnalysisYounes Akbari (16303286)Somaya Al-Maadeed (5178131)Noor Al-Maadeed (16864251)Al Anood Najeeb (16904775)Afnan Al-Ali (16888695)Fouad Khelifi (16904778)Ashref Lawgaly (16904781)EngineeringElectronics, sensors and digital hardwareInformation and computing sciencesComputer vision and multimedia computationCybersecurity and privacyData management and data scienceMachine learningDatabasesCamerasForensicsDeep learningTrainingSocial networking (online)Object recognitionSmart phoneSource camera identification on videosDeep learning methods<p>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.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2022.3151406" target="_blank">https://dx.doi.org/10.1109/access.2022.3151406</a></p>2022-02-14T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2022.3151406https://figshare.com/articles/journal_contribution/A_New_Forensic_Video_Database_for_Source_Smartphone_Identification_Description_and_Analysis/24056388CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240563882022-02-14T00:00:00Z |
| spellingShingle | A New Forensic Video Database for Source Smartphone Identification: Description and Analysis Younes Akbari (16303286) Engineering Electronics, sensors and digital hardware Information and computing sciences Computer vision and multimedia computation Cybersecurity and privacy Data management and data science Machine learning Databases Cameras Forensics Deep learning Training Social networking (online) Object recognition Smart phone Source camera identification on videos Deep learning methods |
| status_str | publishedVersion |
| 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 | A New Forensic Video Database for Source Smartphone Identification: Description and Analysis |
| topic | Engineering Electronics, sensors and digital hardware Information and computing sciences Computer vision and multimedia computation Cybersecurity and privacy Data management and data science Machine learning Databases Cameras Forensics Deep learning Training Social networking (online) Object recognition Smart phone Source camera identification on videos Deep learning methods |