Department of Computer Science & Engineering

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Authentication is one of the most basic and important cryptographic tasks. Mutual authentication protocols play a crucial role on the security of RFID systems. In this paper, we consider the security of a recently proposed mutual authentication protocol by Wei et al. which is a hash based protocol. We present efficient tag impersonation attack, two desynchronization attacks, reader impersonation attack and traceability attack against this protocol. The success probabilities of the attacks are "1" or 1-2-(n-1), where n is the length of the secret value shared between the tag and the reader. The complexity of each one of the presented attacks is only two runs of protocol. Vulnerabilities presented in the present work rule out the practical usage of this protocol. To the best of our knowledge, this is the first security analysis of Wei et al.'s protocol. Finally, we exhibit an improved version of this protocol, which is immune against the attacks presented in this work. © Springer-Verlag Berlin Heidelberg 2012.

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

On the security of mutual authentication protocols for RFID systems: The case of Wei et al 's protocol

4th International Conference on Intelligent Human Computer Interaction: Advancing Technology for Humanity, IHCI 2012

Opening remarks

Event driven data gathering or convergecast through sensor nodes requires efficient and correct delivery of data at the sink. A tree rooted at the sink is an ideal topology for data gathering which utilizes sensor resources properly. Resource constrained sensor nodes are highly prone to sudden crash. A set of algorithms, proposed in this paper, builds a data gathering tree rooted at the sink. The tree eventually becomes a Breadth First Search(BFS) tree where each node maintains the shortest distance in hop-count to the root to reduce the routing delay and power consumption. The data gathering tree is repaired locally within a constant round of message transmissions after any random node fails. Simulation result shows that the repairing delay is very less in average, and the proposed scheme can repair from arbitrary node failure using constant number of message passing. © 2012 IEEE.

Proceedings of the 2012 IEEE Network Operations and Management Symposium, NOMS 2012

A novel crash-tolerant data gathering in wireless sensor networks

The use of many core architectures and accelerators, such as GPUs, with good programmability has allowed them to be deployed for vital computational work. The ability to use randomness in computation is known to help in several situations. For such computations to be made possible on a general purpose computer, a source of randomness, or in general a pseudo random generator (PRNG), is essential. However, most of the PRNGs currently available on GPUs suffer from some basic drawbacks that we highlight in this paper. It is of high interest therefore to develop a parallel, quality PRNG that also works in an on demand model. In this paper we investigate a CPU+GPU hybrid technique to create an efficient PRNG. The basic technique we apply is that of random walks on expander graphs. Unlike existing generators available in the GPU programming environment, our generator can produce random numbers on demand as opposed to a one-time generation. Our approach produces 0.07 GNumbers per second. The quality of our generator is tested with industry standard tests. We also demonstrate two applications of our PRNG. We apply our PRNG to design a list ranking algorithm which demonstrates the on-demand nature of the algorithm and a Monte Carlo simulation which shows the high quality of our generator. © 2012 IEEE.

Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2012

An on-demand fast parallel pseudo random number generator with applications

The work described in the paper deals with realization of a digit recognition system for the visually impaired persons. The input to the system is pressure sensor data obtained from a sensor pad with 256 capacitive pressure sensors. The objective of the system is to recognize correctly the 2-D shape of a digit which is placed on the above sensor pad. The problem is extremely challenging because the input data i) is very susceptible to noise and ii) may undergo rigid body transformation with respect to the reference library of the system During the first step of the recognition process, the Euler number is used to achieve a preliminary classification. In the second step, 4th order central moment is applied to correctly recognize the input. A comparison of the performance of the proposed method with some other existing approaches indicates our recognition results to be highly promising. © 2012 IEEE.

Proceedings of the 2012 International Conference on Communications, Devices and Intelligent Systems, CODIS 2012

Digit recognition from pressure sensor data using Euler number and central moments

Information management systems today face a tremendous challenge considering the growing popularity of social media repositories involving images and video. Considering the growing volume of multimedia content in such online media-sharing communities there is an increasing need for novel ways of organizing content. In this paper we consider the problem of organizing images in a given Flickr Group by discovering latent subgroups. A Flickr Group can be visualized as a collection of such subgroups where each subgroup represents a distinct theme. We model the task of discovering subgroups as that of finding highly correlated topics from a dataset containing images and associated tags. The proposed probabilistic model employs a more flexible prior distribution to model topic-topic correlations and utilizes both tag and image information for discovering such subgroups. Our experiments on Flickr Group data demonstrate that the model is able to successfully discover subgroups without any supervision. © 2012 IEEE.

Proceedings - IEEE International Conference on Multimedia and Expo

Finding subgroups in a Flickr Group

In this paper, we address the problem of discovering activities and their temporal significance in an area under surveillance. Discovering activities along with its expectation of occurrence at a particular time plays an important role in many surveillance applications. We propose an unsupervised model, called Time pLSA model, that extends the probabilistic Latent Semantic Analysis (pLSA) model to jointly capture the activities and their behaviour over time. We use adaptive background subtraction to detect spatio-temporal patches, which are used as feature representation for activity patterns. Each of these patches are associated with the time slot in which they occur. Multinomial distributions are used to model both activities as distribution over spatio-temporal patches and time significance as distribution over the time-line. We demonstrate the effectiveness of our approach on a real life surveillance feed of an outdoor scene. © 2012 IEEE.

Proceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012

Discovering activities and their temporal significance

In any remote surveillance scenario, smart cameras have to take intelligent decisions to generate summary frames to minimize communication and processing overhead. Video summary generation, in the context of smart camera, is the process of merging the information from multiple frames. A summary generation scheme based on clustering based change detection algorithm has been implemented in our smart camera system for generating frames to deliver requisite information. In this paper we propose an embedded platform based framework for implementing summary generation scheme using HW-SW co-design based methodology. The complete system is implemented on Xilinx XUP Virtex-II Pro FPGA board. The overall algorithm is running on PowerPC405 and some of the blocks which are computationally intensive and more frequently called are implemented in hardware using VHDL. The system is designed using Xilinx Embedded Design Kit (EDK). © (2012) Trans Tech Publications, Switzerland.

Advanced Materials Research

FPGA based embedded implementation of video summary generation scheme in smart camera

Scene text recognition has gained significant attention from the computer vision community in recent years. Recognizing such text is a challenging problem, even more so than the recognition of scanned documents. In this work, we focus on the problem of recognizing text extracted from street images. We present a framework that exploits both bottom-up and top-down cues. The bottom-up cues are derived from individual character detections from the image. We build a Conditional Random Field model on these detections to jointly model the strength of the detections and the interactions between them. We impose top-down cues obtained from a lexicon-based prior, i.e. language statistics, on the model. The optimal word represented by the text image is obtained by minimizing the energy function corresponding to the random field model. We show significant improvements in accuracies on two challenging public datasets, namely Street View Text (over 15%) and ICDAR 2003 (nearly 10%). © 2012 IEEE.

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Top-down and bottom-up cues for scene text recognition

This paper presents a novel fusion approach to combine scores from different biometric classifiers using Relevance Vector Machine. RVM uses a combination of kernel functions on training data for classification and compared to SVM, it requires significantly reduced number of relevance vectors. The proposed RVM based fusion algorithm is evaluated using a case study on multi-unit iris recognition. Experimental results on the CASIA-Iris-V4 Thousand database show that RVM provides better accuracy compared to single unit iris recognition and existing fusion algorithms. With respect to SVM fusion, it is observed that, the accuracy of RVM and SVM are comparable, however, the time for RVM fusion is significantly reduced. © 2012 IEEE.