Perceptual Hashing and Watermarking Technologies for Security Applications
Prof A Bouridane
Head – Computer and Electronic Security Systems
Northumbria University at Newcastle (UK)
The field of digital security has witnessed an explosive growth during the last years, as phenomenal advances both in research and applications have been made. Global biometric and forensic market is forecast to reach US$15 billion by 2016. The market is mainly driven by an increasing need for security against terrorist activity, sophisticated crimes and financial frauds. Legislative compulsions in major markets worldwide including North America, Europe and The Middle East are also expected to play an important role in furthering the cause of Biometrics. The technology is forecast to witness an accelerated pace of growth in the next decade, with main emphasis on development of solutions.
Forensic imaging applications often involve photographs, videos and other image impressions that are fragile and include subtle details that are difficult to see. As a developer, one needs to be able to quickly develop sophisticated imaging applications that allow for an accurate extraction of precious information from image data for identification and recognition purposes. This is true for any type of biometric and forensic image data. In addition, there is a need to protect digital media content especially biometric data that is being wildly and widely distributed and shared through the Internet by an ever-increasing number of decentralised users. Digital data hiding and steganography are useful and operate by embedding auxiliary information for use as digital signatures for use to authenticate digital media.
One of the most distinctive features of this seminar, Professor Bouridane will cover covers a number of imaging applications and their deployment in security problems including recent advances in perceptual hashing and digital watermarking and visualisation of Crimes from Multimodal Visual Sensor Networks for security applications.
Professor Bouridane has more than 20 years of experience in image processing and computer vision applications. He received an “Ingenieur d’Etat” degree in electronics from “Ecole Nationale Polytechnique” of Algiers (ENPA), Algeria, in 1982, an M.Phil. degree in electrical engineering (VLSI design for signal processing) from the University of Newcastle-Upon-Tyne, U.K., in 1988, and an Ph.D. degree in electrical engineering (computer vision) from the University of Nottingham, U.K., in 1992. From 1992 to 1994, he worked as a Research Developer in telesurveillance and access control applications. In 1994, he joined Queen’s University Belfast, Belfast, U.K., initially as Lecturer in computer architecture and image processing and later on he was promoted to Reader in Computer Science. Professor Bouridane received the outstanding research award from the European Center for Secure Information and Systems for research contributions in the area of Secure Information Security (2006). He was Visiting Professor at University of Nancy (France): 2006 – 2008 and University of Metz, France): 2009-2011 and 2014. He recently joined Northumbria University to take up a chair in Image Engineering and Security.
Date: Wednesday 18/2/2015
Time: 12.00 pm
Speaker: Dr. Martin Trefzer
Affiliation: Department of Electronics, University of York
Sense, Adapt, Survive: From Biology to Evolutionary Hardware
Dr. Martin Trefzer
Department of Electronics, University of York
The increasing versatility, performance, compactness and power efficiency of today’s electronic systems is achieved by pushing technology to its physical limits: systems are increasing in size and complexity comprising thousands of subsystems made of billions of devices, requiring sophisticated programming and control; the devices themselves become smaller and smaller and have reached the atomic atomic scale, which leads to stochastic variations when fabricating them. This makes components more noisy and unreliable and designing reliable systems extremely challenging. In this respect, technological systems are far behind biological organisms which have long since accomplished the feat of not only operating reliably with highly variable components, but also maintaining and tuning themselves in changing environments, when faults occur or they are otherwise perturbed. Biological mechanisms enabling this have co-evolved with the organisms, hence, are perfectly adapted to the requirements of their embodiment. In this context, evolutionary hardware is about hardware that offers the capability to change its structure and behaviour in order to automatically optimise its operation for a specific task or environment, taking inspiration from biological organisms with natural evolution as Nature’s guiding optimisation principle. The talk will give examples of hardware systems, biological systems, and how the former can learn from the latter.
Dr. Martin Trefzer is a Lecturer in the Department of Electronics at York. His research interests include variability aware analogue and digital hardware design, biologically motivated models of hardware design, evolutionary computation, and autonomous fault-tolerance. His current focus is on using bio-inspired techniques to create adaptive, self-healing multi-reconfigurable architectures to tackle nano-scale CMOS design challenges. Dr Trefzer is co-investigator on 3 currently running EPSRC projects Platform Grant - Bio-inspired Adaptive Architectures and Systems (EP/K040820/1), Graceful (EP/L000563/1) and PAnDA (EP/I005838/1). He is a senior member of the IEEE, a member of the DPG, co-chair of the International Conference of Evolvable Systems (ICES), and vice chair of the IEEE Task Force on Evolvable Hardware.
Date: Friday 12/12/2014
Time: 11.00 am
Speaker: Dr. Ram Bilas Pachori
Affiliation: Indian Institute of Technology Indore India
Features based on the non-stationary signal models for analysis and classification of EEG signals
By Dr. Ram Bilas Pachori
Indian Institute of Technology Indore India
Presentation slides can be downloaded here
Electroencephalogram (EEG) signals measure electrical activity of the human brain. These signals are commonly used signals to assess neurological activities in the brain, and disorder like epilepsy. Recently, our group has developed novel features based on the non-stationary signal models like as empirical mode decomposition, smoothed pseudo Wigner-Ville distribution, and multi-wavelet transform for analysis of classification of EEG signals. These proposed features have been applied for analysis and classification of epileptic seizure, sleep stages, and human emotions (happy, fear, sad, and neutral) from EEG signals. My talk will cover details about the proposed features by our group for analysis and classification of EEG signals corresponding to the above mentioned states of the human brain.
Ram Bilas Pachori received the B.E. degree with Honors in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India, in 2001. He received the M. Tech. and Ph. D. degrees, both in Electrical Engineering from Indian Institute of Technology Kanpur, Kanpur, India, in 2003 and 2008 respectively. From April 2007 to March 2008, he worked as a Postdoctoral Fellow at the University of Technology of Troyes, Troyes, France. In April 2008, he joined the International Institute of Information Technology, Hyderabad, India, as Assistant Professor. In December 2009, he joined the Discipline of Electrical Engineering of Indian Institute of Technology Indore, Indore, India where he is an Associate Professor. His research interests include Bio-medical Signal Processing, Speech Signal Processing, Time-Frequency Analysis, and Signal Processing Applications. He has published more than 60 papers in the reputed journals, conference proceedings, book, and book chapters. He is reviewer of IEEE, Elsevier, and Springer journals..
Date: Wednesday 17/12/2014
Time: 12.00 pm
Speaker: Prof. Dr. Tim Gollisch
Affiliation: University Medical Centre Gottingen, Germany
Signal processing and neural coding in the vertebrate retina
Prof. Dr. Tim Gollisch
University Medical Centre Gottingen, Germany
Presentation slides can be downloaded here
The neural network of the retina is the first stage of visual processing in vertebrates. Traditionally, this network has often been considered as performing a simple linear filtering operation on the visual signals before transmitting the filtered signals to downstream brain areas for the actual computational analysis of the visual scene. Recently, however, it has become increasingly clear that multiple subcircuits inside the retinal network perform specialized, highly nonlinear computations, aimed at solving specific visual tasks. In this talk, I will discuss several examples of how these circuits and their computational functions can be analyzed by combining electrophysiological recordings from the retina and computational modeling.
Tim Gollisch received a Diploma thesis in Physics from the University of Heidelberg in 2000. He received a Ph.D in Biophysics from Humboldt University Berlin in 2004 under the supervision of Andreas V.M. Herz. He then worked as a postdoctoral researcher under the supervision of Markus Meister in Harvard, U.S.A. before taking up a position as Independent Max Planck Research Group Leader at the Max Planck Institute of Neurobiology in Munich, Germany. Since 2010 he has been a Professor for Sensory Processing in the Retina at the University Medical Centre Gottingen, Germany. He has received many fellowships and awards including: Bernard Bernard Katz Lecture Award; Career Development Award of the Human Frontier Science Program; Long-term postdoctoral fellowship of the Human Frontier Science Program; Humboldt Award of the Humboldt University Berlin for dissertation thesis; PhD fellowship from Boehringer Ingelheim Fond; Otto Haxel Award of Heidelberg University for diploma thesis His research interests are in investigating how the neural network in the vertebrate retina processes visual information to understand the relationship between visual stimuli and neural responses. He has published his research in many journals including, Science, Neuron, Journal of Neuroscience, Journal of Physiology, Journal of Computational Neuroscience, PLoS ONE, and Frontiers in Neural Circuits.
Date: Friday 28/11/2014
Time: 11.00 am
Speaker: Dr. Denis O'Hora
Affiliation: National University of Ireland, Galway
Mind in Motion: Can how we choose tell us about why we choose?
Dr. Denis O'Hora
National University of Ireland, Galway
When cognitive processes occur alongside observable actions, it is possible for characteristics of these processes to influence the ongoing performance of those actions. That is, cognitive processes may ‘leak’ into motor processes. Anecdotally, negotiators and poker players claim to be attuned to ‘tells,’ early behavioural indicators of eventual decisions. Going beyond intuitions, however, several researchers have exploited fine-grained measures of behaviour to highlight the effects of online cognitive processing.
In Dr O'Hora's research, participants make simple choices using a computer mouse, which provides a rich semi-continuous stream of action information. By tracking 'how' participants make their decisions, it is possible to infer characteristics of participants' evaluations of the alternatives available to them. He will summarise recent findings from his laboratory and some of the novel analytic techniques that he and his collaborators have developed.
Denis O’Hora has over 30 publications in the areas of learning, organizational psychology, and neuroscience. The primary focus of his research is on the dynamics of human learning and decision-making. From 2003 to 2007, he worked in the School of Psychology in the University of Ulster at Coleraine before moving to the National University of Ireland Galway, where he currently works. He is a member of the Brain and Behaviour group in the School of Psychology and participates in both the Complex Systems Research Centre (CORE) and the Galway Neuroscience Centre.
- Experience-based goal generation and motivated reinforcement learning for a mobile robot
- Braitenberg vehicles: From Theory to Implementation
- Evolving Spiking Neural Networks: Methods, Systems and Applications for Spatio- and Spectro-Temporal Pattern Recognition
- Development of the RUBICON (Robotic UBIquitous COgnitive Network) cognitive architecture