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Title: Digital camera identification using sensor pattern noise for forensics applications
Author: Lawgaly, Ashref
ISNI:       0000 0004 6425 7083
Awarding Body: Northumbria University
Current Institution: Northumbria University
Date of Award: 2017
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Nowadays, millions of pictures are shared through the internet without applying any authentication system. This may cause serious problems, particularly in situations where the digital image is an important component of the decision making process for example, child pornography and movie piracy. Motivated by this, the present research investigates the performance of estimating Photo Response Non-Uniformity (PRNU) and developing new estimation approaches to improve the performance of digital source camera identification. The PRNU noise is a sensor pattern noise characterizing the imaging device. Nonetheless, the PRNU estimation procedure is faced with the presence of image-dependent information as well as other non-unique noise components. This thesis primarily focuses on efficiently estimating the physical PRNU components during different stages. First, an image sharpening technique is proposed as a pre-processing approach for source camera identification. The sharpening method aims to amplify the PRNU components for better estimation. In the estimation stage, a new weighted averaging (WA) technique is presented. Most existing PRNU techniques estimate PRNU using the constant averaging of residue signals extracted from a set of images. However, treating all residue signals equally through constant averaging is optimal only if they carry undesirable noise of the same variance. Moreover, an improved version of the locally adaptive discrete cosine transform (LADCT) filter is proposed in the filtering stage to reduce the effect of scene details on noise residues. Finally, the post-estimation stage consists of combining the PRNU estimated from each colour plane aims to reduce the effect of colour interpolation and increasing the amount of physical PRNU components. The aforementioned techniques have been assessed on two image datasets acquired by several camera devices. Experimental results have shown a significant improvement obtained with the proposed enhancements over related state-of-the-art systems. Nevertheless, in this thesis the experiments are not including images taken with various acquisition different resolutions to evaluate the effect of these settings on PRNU performance. Moreover, images captured by scanners, cell phones can be included for a more comprehensive work. Another limitation is that investigating how the improvement may change with JPEG compression or gamma correction. Additionally, the proposed methods have not been considered in cases of geometrical processing, for instance cropping or resizing.
Supervisor: Khelifi, Fouad Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: G700 Artificial Intelligence