Sensor Pattern Noise Estimation using Non-Textured Video Frames for Efficient Source Smartphone Identification and Verification

Ashref Lawgaly, Fouad Khelifi, Ahmed Bouridane, Somaya Al-Maaddeed

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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Abstract

Photo response non-uniformity (PRNU) noise is a sensor pattern noise characterizing the imaging device. It has been broadly used in the literature for image authentication and source camera identification. The abundant information that the PRNU carries in terms of the frequency content makes it unique, and therefore suitable for identifying the source camera and detecting forgeries in digital images. However, PRNU estimation from smartphone videos is a challenging process due to the presence of frame-dependent information (very dark/very textured), as well as other non-unique noise components and distortions due to lossy compression. In this paper, we propose an approach that considers only the non-Textured frames in estimating the PRNU because its estimation in highly textured images has been proven to be inaccurate in image forensics. Furthermore, lossy compression distortions tend to affect mainly the textured and high activity regions and consequently weakens the presence of the PRNU in such areas. The proposed technique uses a number of texture measures obtained from the Grey Level Cooccurrence Matrix (GLCM) prior to an unsupervised learning process that splits the feature space through training video frames into two different sub-spaces, i.e., the textured space and the non-Textured space. Non-Textured video frames are filtered out and used for estimating the PRNU. Experimental results on a public video dataset captured by various smartphone devices have shown a significant gain obtained with the proposed approach over the conventional state-of-The-Art approach.

Original languageEnglish
Title of host publicationProceedings - 2021 International Conference on Computing, Electronics and Communications Engineering, iCCECE 2021
EditorsMahdi H. Miraz, Garfield Southall, Maaruf Ali, Andrew Ware, Safeeullah Soomro
Place of PublicationPiscataway
PublisherIEEE
Pages19-24
Number of pages6
ISBN (Electronic)9781665449113, 9781665449106
ISBN (Print)9781665449120
DOIs
Publication statusPublished - 16 Aug 2021
Event4th International Conference on Computing, Electronics and Communications Engineering, iCCECE 2021 - Virtual, Online, United Kingdom
Duration: 16 Aug 202117 Aug 2021

Conference

Conference4th International Conference on Computing, Electronics and Communications Engineering, iCCECE 2021
Country/TerritoryUnited Kingdom
CityVirtual, Online
Period16/08/2117/08/21

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