A New Dataset for Forged Smartphone Videos Detection: Description and Analysis
The advancement of Internet technology has significantly impacted daily life, which is influenced by digital videos taken with smartphones as the most popular type of multimedia. These digital videos are extensively sent through various social media websites such as WhatsApp, Instagram, Facebook, Tw...
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10103517/ |
| _version_ | 1797776954582106112 |
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| author | Younes Akbari Al Anood Najeeb Somaya Al Maadeed Omar Elharrouss Fouad Khelifi Ashref Lawgaly |
| author_facet | Younes Akbari Al Anood Najeeb Somaya Al Maadeed Omar Elharrouss Fouad Khelifi Ashref Lawgaly |
| author_sort | Younes Akbari |
| collection | DOAJ |
| description | The advancement of Internet technology has significantly impacted daily life, which is influenced by digital videos taken with smartphones as the most popular type of multimedia. These digital videos are extensively sent through various social media websites such as WhatsApp, Instagram, Facebook, Twitter, and YouTube. The development of intelligent and simple editing tools has favoured the transformation of multimedia content on the Internet. As a result, these digital videos’ credibility, reliability, and integrity have become critical concerns. This paper presents a video forgery (Copy-move forgery) dataset in which 250 original videos are manipulated mainly by two forgery techniques: Insertion and Deletion. Inserting transparent objects into the original video without raising suspicion is one type of manipulation performed. Another type of forgery presented on the dataset is the removal of objects from the original video without notifying the viewers. The videos were collected from five different mobile devices, namely, IPhone 8 Plus, Nokia 5.4, Samsung A50, Xiomi Redmi Note 9 Pro and Huawei Y9-1. The forged videos were created using a popular video editing software called Adobe After Effects as well as usage of other software such as Adobe Photoshop and AfterCodecs. Since the source of the videos is known, PRNU-based methods can be suitable for applying to the dataset. Experiments were performed using classical and deep learning methods. The results are recorded and discussed in detail, showing that improvements are essential for the dataset. Furthermore, the forged videos of this dataset are comparatively large when compared to other datasets that performed copy-move forgery. |
| first_indexed | 2024-03-12T22:57:12Z |
| format | Article |
| id | doaj.art-bf4dedbecebb471ab3c45eec38b241b6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-12T22:57:12Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-bf4dedbecebb471ab3c45eec38b241b62023-07-19T23:00:40ZengIEEEIEEE Access2169-35362023-01-0111703877039510.1109/ACCESS.2023.326774310103517A New Dataset for Forged Smartphone Videos Detection: Description and AnalysisYounes Akbari0https://orcid.org/0000-0001-7175-4326Al Anood Najeeb1https://orcid.org/0000-0001-8764-4201Somaya Al Maadeed2https://orcid.org/0000-0002-0241-2899Omar Elharrouss3https://orcid.org/0000-0002-5341-5440Fouad Khelifi4https://orcid.org/0000-0001-7413-0025Ashref Lawgaly5https://orcid.org/0000-0001-6715-1645Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, QatarDepartment of Computer Science and Engineering, College of Engineering, Qatar University, Doha, QatarDepartment of Computer Science and Engineering, College of Engineering, Qatar University, Doha, QatarDepartment of Computer Science and Engineering, College of Engineering, Qatar University, Doha, QatarDepartment of Computer and Information Sciences, Northumbria University, Newcastle Upon Tyne, U.KDepartment of Computer and Information Sciences, Northumbria University, Newcastle Upon Tyne, U.KThe advancement of Internet technology has significantly impacted daily life, which is influenced by digital videos taken with smartphones as the most popular type of multimedia. These digital videos are extensively sent through various social media websites such as WhatsApp, Instagram, Facebook, Twitter, and YouTube. The development of intelligent and simple editing tools has favoured the transformation of multimedia content on the Internet. As a result, these digital videos’ credibility, reliability, and integrity have become critical concerns. This paper presents a video forgery (Copy-move forgery) dataset in which 250 original videos are manipulated mainly by two forgery techniques: Insertion and Deletion. Inserting transparent objects into the original video without raising suspicion is one type of manipulation performed. Another type of forgery presented on the dataset is the removal of objects from the original video without notifying the viewers. The videos were collected from five different mobile devices, namely, IPhone 8 Plus, Nokia 5.4, Samsung A50, Xiomi Redmi Note 9 Pro and Huawei Y9-1. The forged videos were created using a popular video editing software called Adobe After Effects as well as usage of other software such as Adobe Photoshop and AfterCodecs. Since the source of the videos is known, PRNU-based methods can be suitable for applying to the dataset. Experiments were performed using classical and deep learning methods. The results are recorded and discussed in detail, showing that improvements are essential for the dataset. Furthermore, the forged videos of this dataset are comparatively large when compared to other datasets that performed copy-move forgery.https://ieeexplore.ieee.org/document/10103517/Datasetvideomobile devicescopy-move forgerydeep learning |
| spellingShingle | Younes Akbari Al Anood Najeeb Somaya Al Maadeed Omar Elharrouss Fouad Khelifi Ashref Lawgaly A New Dataset for Forged Smartphone Videos Detection: Description and Analysis IEEE Access Dataset video mobile devices copy-move forgery deep learning |
| title | A New Dataset for Forged Smartphone Videos Detection: Description and Analysis |
| title_full | A New Dataset for Forged Smartphone Videos Detection: Description and Analysis |
| title_fullStr | A New Dataset for Forged Smartphone Videos Detection: Description and Analysis |
| title_full_unstemmed | A New Dataset for Forged Smartphone Videos Detection: Description and Analysis |
| title_short | A New Dataset for Forged Smartphone Videos Detection: Description and Analysis |
| title_sort | new dataset for forged smartphone videos detection description and analysis |
| topic | Dataset video mobile devices copy-move forgery deep learning |
| url | https://ieeexplore.ieee.org/document/10103517/ |
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