On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation

Al-Ani, Mustafa and Khelifi, Fouad (2016) On the Sensor Pattern Noise Estimation in Image Forensics: A Systematic Empirical Evaluation. IEEE Transactions on Information Forensics and Security, 12 (2). pp. 1067-1081. ISSN 1556-6013

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Official URL: http://dx.doi.org/10.1109/TIFS.2016.2640938

Abstract

Extracting a fingerprint of a digital camera has fertile applications in image forensics, such as source camera identification and image authentication. In the last decade, Photo Response Non_Uniformity (PRNU) has been well established as a reliable unique fingerprint of digital imaging devices. The PRNU noise appears in every image as a very weak signal, and its reliable estimation is crucial for the success rate of the forensic application. In this paper, we present a novel methodical evaluation of 21 state-of-the-art PRNU estimation/enhancement techniques that have been proposed in the literature in various frameworks. The techniques are classified and systematically compared based on their role/stage in the PRNU estimation procedure, manifesting their intrinsic impacts. The performance of each technique is extensively demonstrated over a large-scale experiment to conclude this case-sensitive study. The experiments have been conducted on our created database and a public image database, the 'Dresden image database

Item Type: Article
Additional Information: © 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: sensor pattern noise (SPN), Authentication, camera identification, digital forensics, photo response non-uniformity (PRNU)
Subjects: G400 Computer Science
Department: Faculties > Engineering and Environment > Computer Science and Digital Technologies
Depositing User: Ay Okpokam
Date Deposited: 24 Jan 2017 13:37
Last Modified: 08 May 2017 21:09
URI: http://nrl.northumbria.ac.uk/id/eprint/29339

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