Soobhany, Ahmad Ryad (2013) Image source identification and characterisation for forensic analysis. Doctoral thesis, Keele University.

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Abstract

Digital imaging devices, such as digital cameras or mobile phones, are prevalent in society. The images created by these devices can be used in the commission of crime. Source device identification is an emerging research area and involves the identification of artefacts that are left behind in an image by the camera pipeline. These artefacts can be used as digital signatures to identify the source device forensically. The type of digital signature considered in this thesis is the Sensor Pattern Noise (SPN), which consists mainly of the PRNU (Photo Response Non-Uniformity) of the imaging device. The PRNU is unique to each individual sensor, which can be extracted traditionally with a wavelet denoising filter and enhanced to attenuate unwanted artefacts.
This thesis proposes a novel method to extract the PRNU of a digital image by using Singular Value Decomposition (SVD) to extract the digital signature. The extraction of the PRNU is performed using the homomorphic filtering technique, where the inherently nonlinear PRNU is transformed into an additive noise. The range of the energy of the PRNU is estimated, which makes it easier to separate from other polluting components to obtain a cleaner signature, as compared to extracting all the high frequency signals from an image. The image is decomposed by using SVD, which separates the image into ranks of descending order of energies. The estimated energy range of the PRNU is used to obtain the interesting ranks that are utilised to form part of the digital signature. A case study of an existing image analyser platform was performed by investigating its identification and classification results. The SVD based extraction method was tested by extracting image signatures from camera phones. The results of the experiments show that it is possible to determine the source device of digital images.

Item Type: Thesis (Doctoral)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Natural Sciences > School of Computing and Maths
Depositing User: Lisa Bailey
Date Deposited: 20 Oct 2016 11:16
Last Modified: 20 Oct 2016 11:16
URI: http://eprints.keele.ac.uk/id/eprint/2301

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