Digital forensic techniques for the reverse engineering of image acquisition chains

120 Pages·2014·22.38 MB·English

Digital forensic techniques
for the reverse engineering of image
acquisition chains
by
Thirapiroon Thongkamwitoon
A Thesis submitted in fulfilment of requirements for the degree of
Doctor of Philosophy of Imperial College London
Communications and Signal Processing Group
Department of Electrical and Electronic Engineering
Imperial College London
2014

Statement of Originality
I declare that the intellectual content of this thesis is the product of my own research work
under the guidance of my thesis advisor, Dr. Pier Luigi Dragotti. Any ideas or quotations
from the work of other people, published or otherwise, are fully acknowledged in accordance
with the standard referencing practices of the discipline. The material of this thesis has not
been submitted for any degree at any other academic or professional institution.
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Copyright
The copyright of this thesis rests with the author and is made available under a Creative
Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy,
distribute or transmit the thesis on the condition that they attribute it, that they do not use
it for commercial purposes and that they do not alter, transform or build upon it. For any
reuse or redistribution, researchers must make clear to others the licence terms of this work.
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Abstract
In recent years a number of new methods have been developed to detect image forgery. Most
forensic techniques use footprints left on images to predict the history of the images. The
images, however, sometimes could have gone through a series of processing and modification
through their lifetime. It is therefore difficult to detect image tampering as the footprints
could be distorted or removed over a complex chain of operations. In this research we propose
digital forensic techniques that allow us to reverse engineer and determine history of images
that have gone through chains of image acquisition and reproduction.
This thesis presents two different approaches to address the problem. In the first part we
propose a novel theoretical framework for the reverse engineering of signal acquisition chains.
Basedonasimplifiedchainmodel, wedescribehowsignalshavegoneinthechains atdifferent
stages using the theory of sampling signals with finite rate of innovation. Under particular
conditions, our technique allows to detect whether a given signal has been reacquired through
the chain. It also makes possible to predict corresponding important parameters of the chain
using acquisition-reconstruction artefacts left on the signal.
The second part of the thesis presents our new algorithm for image recapture detection
based on edge blurriness. Two overcomplete dictionaries are trained using the K-SVD ap-
proach to learn distinctive blurring patterns from sets of single captured and recaptured im-
ages. AnSVMclassifieristhenbuiltusingdictionaryapproximationerrorsandthemeanedge
spread width from the training images. The algorithm, which requires no user intervention,
was tested on a database that included more than 2500 high quality recaptured images. Our
results show that our method achieves a performance rate that exceeds 99% for recaptured
images and 94% for single captured images.
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Acknowledgement
I would like to express my gratitude to different persons who have all contributed to my
memorable journey towards a PhD at Imperial College London.
First and foremost, I would like to deeply thank to my PhD supervisor, Dr. Pier Luigi
Dragotti, for his constant support, guidance, and understanding. I am grateful to him for
offering me the chance to undertake a PhD in London and all the opportunities over the past
4 years. Despite being very busy, he always arranges a weekly meeting for his students with
enthusiasm and I have left every meeting wiser and more motivated. He is my role model
in how to work professionally. Without his invaluable advices and patience I would not have
been able to have come this far.
IalsowouldliketothanktoDr. HaniMuammar,apost-doctoralcolleagueforhisclosecol-
laborations, suggestions, and useful support on writing. His dedication to build our database
of professionally recaptured images has great contributions to this research. I would like to
acknowledge all colleagues in the REWIND project for sharing knowledge and opportunities.
In addition, I am thankful to the Office of the National Broadcasting and Telecommu-
nications Commission (NBTC), Thailand, for a scholarship support throughout my PhD. In
particular, I would like to thank to Mr Takorn Tantasith, the Secretary General of NBTC,
and Assoc. Professor Sudharma Yoonaidharma, a former NBTC Commissioner, who initiated
the scholarship project. I am also indebted to Narumol Roylapcharoenporn, a scholarship
collaborator of the NBTC, for all her supports and understanding.
I would then like to thank all my friends and colleagues from the C&SP research group
including Marco, Jon, Toni, Adam, James, Liang, and Shiqiang for suggestions and the good
moments we had in London and at conferences abroad. I also wish to thank all Thai friends
at Imperial, especially Khoonsake, Ohm, Chedtha, Chon, Tos, Jeep, Moo, Nong, Kendo, Moc,
Toey for helps, motivation, and constructive discussions.
Last but not least, I am thankful to Wanwisa Chaisaklert, who has accompanied me
since the first day of my journey. Her support, love, and encouragement have made me keep
walking forward. Finally, I am forever grateful to my Dad and my Mum, Mr Sathian and Mrs
Angkana Thongkamwitoon, who have been all inspirations and role models. I deeply thank
to their unconditional and incessant support and love.
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