Soobhany, Ahmad Ryad, Lam, KP, Collins, D and Fletcher, P (2014) Mobile Camera Source Identification with SVD. Lecture Notes in Electrical Engineering, 313. pp. 123-131.

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Abstract

A novel method for extracting the characterising sensor pattern noise (SPN) from digital images is presented. Based on the spectral decomposition technique of Singular Value Decomposition, the method estimates the SPN of each image in terms of its energy level by first transforming the image/signals into a linear additive noise model that separates the photo response non-uniformity (PRNU) of the associated camera from the signal subspace. The camera reference signatures of the individual cameras are computed from a sample of their respective images and compared with a mixture of image signatures from a set of known camera devices. The statistical properties of the method were studied using the Student’s t-test constructed under the null hypothesis formalism. Our studies show that it is possible to determine the source device of digital images from camera phones using such method of signature extraction, with encouraging results.

Item Type: Article
Additional Information: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via Springer Nature at - This is the accepted author manuscript (AAM). The final published version (version of record) is available online via [insert publisher name] at [insert hyperlink]. Please refer to any applicable terms of use of the publisher. Please refer to any applicable terms of use of the publisher. Conference Paper published within "Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering", Lecture Notes in Electrical Engineering series volume 313.
Uncontrolled Keywords: source identification, singular value decomposition, digital image forensics, sensor pattern noise, PRNU, mobile camera phone
Depositing User: Symplectic
Date Deposited: 07 Feb 2017 11:21
Last Modified: 15 May 2019 15:05
URI: http://eprints.keele.ac.uk/id/eprint/2854

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