TY - JOUR
T1 - A New Dataset for Forged Smartphone Videos Detection
T2 - Description and Analysis
AU - Akbari, Younes
AU - Najeeb, Al Anood
AU - Al-Maadeed, Somaya
AU - Elharrouss, Omar
AU - Khelifi, Fouad
AU - Lawgaly, Ashref
N1 - Funding information: This work was supported in part by the National Priorities Research Program (NPRP) from Qatar National Research Fund (a member of
Qatar Foundation) under Grant NPRP12S-0312-190332, and in part by the Qatar National Library (QNL).
PY - 2023/7/17
Y1 - 2023/7/17
N2 - 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.
AB - 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.
KW - Dataset
KW - copy-move forgery
KW - deep learning
KW - mobile devices
KW - video
UR - http://www.scopus.com/inward/record.url?scp=85153532027&partnerID=8YFLogxK
U2 - 10.1109/access.2023.3267743
DO - 10.1109/access.2023.3267743
M3 - Article
SN - 2169-3536
VL - 11
SP - 70387
EP - 70395
JO - IEEE Access
JF - IEEE Access
ER -