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AuthorAkbari, Younes
AuthorAlmaadeed, Noor
AuthorAl-Maadeed, Somaya
AuthorKhelifi, Fouad
AuthorBouridane, Ahmed
Available date2023-02-23T09:13:04Z
Publication Date2022
Publication NameProceedings - International Conference on Pattern Recognition
ResourceScopus
URIhttp://dx.doi.org/10.1109/ICPR56361.2022.9956272
URIhttp://hdl.handle.net/10576/40336
AbstractRecent advances in digital imaging have meant that every smartphone has a video camera that can record high-quality video for free and without restrictions. In addition, rapidly developing Internet technology has contributed significantly to the widespread distribution of digital video via web-based multimedia systems and mobile applications such as YouTube, Facebook, Twitter, WhatsApp, etc. However, as the recording and distribution of digital video has become affordable nowadays, security issues have become threatening and have spread worldwide. One of the security issues is the identification of source cameras on videos. Generally, two common categories of methods are used in this area, namely Photo Response Non-Uniformity (PRNU) and Machine Learning approaches. To exploit the power of both approaches, this work adds a new PRNU-based layer to a convolutional neural network (CNN) called PRNU-Net. To explore the new layer, the main structure of the CNN is based on the MISLnet, which has been used in several studies to identify the source camera. The experimental results show that the PRNU-Net is more successful than the MISLnet and that the PRNU extracted by the layer from low features, namely edges or textures, is more useful than high and mid-level features, namely parts and objects, in classifying source camera models. On average, the network improves the results in a new database by about 4%.
SponsorThis publication was made possible by NPRP grant # NPRP12S-0312-190332 from Qatar National Research Fund (a member of Qatar Foundation). The statement made herein are solely the responsibility of the authors.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectDeep learning
convolutional neural network (CNN)
Learning approach
TitlePRNU-Net: a Deep Learning Approach for Source Camera Model Identification based on Videos Taken with Smartphone
TypeConference Paper
Pagination599-605
Volume Number2022-August
dc.accessType Abstract Only


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