PRNU-Net: a Deep Learning Approach for Source Camera Model Identification based on Videos Taken with Smartphone

Akbari, Younes, Almaadeed, Noor, Al-Maadeed, Somaya, Khelifi, Fouad and Bouridane, Ahmed (2022) PRNU-Net: a Deep Learning Approach for Source Camera Model Identification based on Videos Taken with Smartphone. In: Proceedings of the International Conference on Pattern Recognition (ICPR) 2022. IEEE, Piscataway, NJ. (In Press)

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Abstract

Recent advances in digital imaging have meant that every smartphone has a video camera that can record highquality 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 theresults in a new database by about 4.

Item Type: Book Section
Additional Information: Funding information: This publication was made possible by NPRP grant # NPRP12S-0312-190332 from Qatar National Research Fund (a member of Qatar Foundation) 26th International Conference on Pattern Recognition, ICPR ; Conference date: 21-08-2022 Through 25-08-2022
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Rachel Branson
Date Deposited: 21 Jun 2022 08:27
Last Modified: 27 Oct 2022 12:00
URI: https://nrl.northumbria.ac.uk/id/eprint/49361

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