Citation: Lawgaly, Ashref (2017) Digital camera identification using sensor pattern noise for forensics applications. Doctoral thesis, Northumbria University. This version was downloaded from Northumbria Research Link: http://nrl.northumbria.ac.uk/32314/ Northumbria University has developed Northumbria Research Link (NRL) to enable users to access the University’s research output. Copyright © and moral rights for items on NRL are retained by the individual author(s) and/or other copyright owners. Single copies of full items can be reproduced, displayed or performed, and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided the authors, title and full bibliographic details are given, as well as a hyperlink and/or URL to the original metadata page. The content must not be changed in any way. Full items must not be sold commercially in any format or medium without formal permission of the copyright holder. The full policy is available online: http://nrl.northumbria.ac.uk/policies.html DIGITAL CAMERA IDENTIFICATION USING SENSOR PATTERN NOISE FOR FORENSICS APPLICATIONS Ashref Lawgaly A thesis submitted in partial fulfilment of the requirements of the University of Northumbria at Newcastle for the degree of Doctor of Philosophy Research undertaken in the Faculty of Engineering and Environment January 2017 DECLARATION I declare that no outputs submitted for this degree have been submitted for a research degree of any other institution. I also confirm that this work fully acknowledges opinions, ideas and contributions from the work of others. Any ethical clearance for the research presented in this commentary has been approved. Approval has been sought and granted by the Faculty Ethics Committee. I declare that the Word Count of this Commentary is 34,483 words Name: Ashref Lawgaly Signature: Lawgaly Date: 28/04/2016. ii Acknowledgements First, I would like to express my deep gratitude to my lord, Almighty ALLAH, for generously giving me endless precious bounties. Without His guidance and mercy, this work would have never reached this stage. I would like to state genuine gratitude to Dr. Fouad Khelifi for his generous provision of his time and advice. Personality, my special thanks goes to my wife Sumeia Elkazza for her constant encouragement and support. Also, many thanks for my mother and my mother in low for their support during the study period and their wishes. Finally, I would like to thank all my colleagues for their essential help and encouragement. iii Publications The following papers have been published. A. Lawgaly, F. Khelifi, and A. Bouridane, “Image sharpening for efficient source camera identification based on sensor pattern noise estimation,” in Proc. International Conference on Emerging Security Technologies, Cambridge, UK, Sep. 2013. A. Lawgaly, F. Khelifi, and A. Bouridane, “Weighted averaging based sensor pattern noise estimation for source camera identification,” in Proc. International Conference on Image Processing (ICIP), Paris, France, Oct. 2014. M.Al-Ani, F.Khelifi, A. Lawgaly, and A. Bouridane, “A novel image filtering approach for sensor fingerprint estimation in source camera identification”, Advanced Video and Signal Based Surveillance (AVSS), 12th IEEE International Conference. Karlsruhe, 25-28 Aug. 2015. Shaikh, M.K., Lawgaly, A., Tahir, M.A. and Bouridane, A. (2016) 'Modality identification for heterogeneous face recognition', Multimedia Tools and Applications, pp. 1-16. A. Lawgaly, and F. Khelifi, “Sensor Pattern Noise Estimation Based on Improved Locally Adaptive DCT Filtering and Weighted Averaging for Source Camera Identification and Verification,” IEEE Transactions on Information Forensics and Security. iv ABSTRACT 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. Table of Contents ABSTRACT ......................................................................................................................... i List of Abbreviations ........................................................................................................ vii List of Tables ..................................................................................................................... ix List of Figures ................................................................................................................... xii INTRODUCTION ..................................................................................... 1 1.1 Motivation ............................................................................................................ 1 1.2 Background .......................................................................................................... 2 1.2.1 Digital imaging applications ......................................................................... 2 1.2.2 Digital image authentication ......................................................................... 2 1.2.3 Image acquisition sensors ............................................................................. 4 1.2.4 Digital image acquisition model ................................................................... 6 1.3 Scope of research ................................................................................................. 8 1.4 Objectives ............................................................................................................. 9 1.5 Thesis contributions ........................................................................................... 10 1.6 Thesis outline ..................................................................................................... 13 IMAGE REPRESENTATION IN THE TRANSFORM DOMAIN ....... 14 2.1 Motivations......................................................................................................... 14 2.2 Spatial domain representation ............................................................................ 14 2.3 Discrete Fourier transform ................................................................................. 16 ii 2.4 Discrete cosine transform ................................................................................... 18 2.5 Discrete Wavelet Transform .............................................................................. 19 2.5.1 Multiresolution analysis .............................................................................. 20 2.5.2 Two-dimensional wavelet transform .......................................................... 23 2.6 Spatial and Transform Domain Filtering Methods ............................................ 24 2.7 Conclusion .......................................................................................................... 26 IMAGE FORENSICS AND SECURITY: REVIEW ............................. 27 3.1 Introduction ........................................................................................................ 27 3.2 Application scenarios ........................................................................................