Department of Computer Science & Engineering

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IIT Jodhpur

The architectural trend towards heterogeneity has pushed heterogeneous computing to the fore of parallel computing research. Heterogeneous algorithms, often carefully handcrafted, have been designed for several important problems from parallel computing such as sorting, graph algorithms, matrix computations, and the like. A majority of these algorithms follow a work partitioning approach where the input is divided into appropriate sized parts so that individual devices can process the 'right' parts of the input. However, arriving at a good work partitioning is usually non-trivial and may require extensive empirical search. Such an extensive empirical search can potentially offset any gains accrued out of heterogeneous algorithms. Other recently proposed approaches too are in general inadequate.In this paper, we propose a simple and effective technique for work partitioning in the context of heterogeneous algorithms. Our technique is based on sampling and therefore can adapt to both the algorithm used and the input instance. Our technique is generic in its applicability as we will demonstrate in this paper. We validate our technique on three problems: finding the connected components of a graph (CC), multiplying two unstructured sparse matrices (spmm), and multiplying two scalefree sparse matrices. For these problems, we show that using our method, we can find the required threshold that is under 10% away from the best possible thresholds. © 2017 IEEE.

World University root node

Proceedings of the International Conference on Parallel Processing

Nearly Balanced Work Partitioning for Heterogeneous Algorithms

The construction SPF, presented in Inscrypt-2016 was the first known SPN based format-preserving encryption algorithm. In this work, we significantly improve its performance and flexibility. We term this new construction as eSPF. Unlike SPF, all the basic transformations of eSPF are defined under the field Fp. This allows us to use a MDS matrix instead of the binary matrix used in SPF. The optimal diffusion of MDS matrix leads to an efficient and secure design. However, this change leads to violations in the message format. To mitigate this, we propose a discarding algorithm to drop the symbols that are not the elements of the format thus preserving it. We also present a concrete instantiation of eSPF for digits and its comparison with existing FPE algorithms like FFX and SPF. The performance analysis shows that the proposed design is at least 15 times faster than FFX for most of the practical applications. © Springer International Publishing AG 2017.

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

eSPF: A family of format-preserving encryption algorithms using MDS matrices

In this paper, we present an eight round distinguisher for four-branch type-2 generalized Feistel network (GFN) with double-SP (DSP) functions and two distinguishers for eight-branch type-2 GFN with single-SP (SSP) functions in a known key attack (KKA) model. We improved the result presented by Sasaki in Indocrypt 2012 by extending the number of rounds attacked from seven to eight for four-branch GFN. Furthermore, for eight-branch type-2 GFN with SSP functions, we present the first known key distinguishers. Our attack works up to 15 rounds of this GFN for all practical parameters. Subsequently, we extend the attack to 17 rounds for the same GFN, which works for most practical parameters. On the basis of our second result and the number of rounds attacked, we conclude that eight-branch type-2 GFN with SSP functions is weaker than four-branch type-Two GFN with DSP functions in the KKA model. We apply rebound attack technique to mount all three distinguishers. However, a limitation of all the distinguishers presented in this paper is that they are useful only if the input size of S-boxes in bits is greater than or equal to the number of S-boxes in one S-box layer. © 2013 IEEE.

Fulltext

IEEE Access

Distinguishers for 4-Branch and 8-Branch Generalized Feistel Network

A new method of adaptive rule generation in granular computing framework is described based on rough rule base and granular flow graph, and applied for video tracking. In the process, several new concepts and operations are introduced, and methodologies formulated with superior performance. The flow graph enables in defining an intelligent technique for rule base adaptation where its characteristics in mapping the relevance of attributes and rules in decision-making system are exploited. Two new features, namely, expected flow graph and mutual dependency between flow graphs are defined to make the flow graph applicable in the tasks of both training and validation. All these techniques are performed in neighborhood granular level. A way of forming spatio-temporal 3-D granules of arbitrary shape and size is introduced. The rough flow graph-based adaptive granular rule-based system, thus produced for unsupervised video tracking, is capable of handling the uncertainties and incompleteness in frames, able to overcome the incompleteness in information that arises without initial manual interactions and in providing superior performance and gaining in computation time. The cases of partial overlapping and detecting the unpredictable changes are handled efficiently. It is shown that the neighborhood granulation provides a balanced tradeoff between speed and accuracy as compared to pixel level computation. The quantitative indices used for evaluating the performance of tracking do not require any information on ground truth as in the other methods. Superiority of the algorithm to nonadaptive and other recent ones is demonstrated extensively.

IEEE transactions on cybernetics

Granular Flow Graph, Adaptive Rule Generation and Tracking

The human eye can perceive information from the visible light in terms of bands of three colors (red, green, blue), so generally images store in the digital are made up of three dimensions i.e., R, G and B. But hyper spectral imaging perceives information from across the electromagnetic spectrum; the process of spectral imaging further splits the spectrum into more bands. This process of changing images into bands can be even in the invisible spectrum. Hence the hyper spectral images can be considered as n-dimensional matrices and each pixel can be regarded as n-dimens ional vector. These images contain various areas with similar characteristics like crop fields, forest area and deserts. To classify such regions one has look for certain features among the captured images. Some similarity measures should be undertaken to make clusters of areas having similar characteristics from the images. Finding the relative similarities in terms of numerical score can be carried out with the help of some standard algorithm. So, feature classification on basis of relative similarities pixel is robust method. In this paper proposing classification of hyper spectral images using Multilayer Perceptron Artificial Neural Network (MLPANN) and Functional Link Artificial Neural Network (FLANN) and their performance is compare in term of accuracy rate. © 2017 IEEE.

Proceedings of the 7th International Conference Confluence 2017 on Cloud Computing, Data Science and Engineering

Hyper spectral image classification using multilayer perceptron neural network &amp; functional link ANN

Image and video processing is currently active research field during the past few years. Different coding schemes are available in the literature for image and video compression to improve compression ratio while maintaining picture quality. Many of the algorithms use ROI coding such as saliency based concept using different image features. But very few works related to depth cues preserving compression. In this paper, we present a behavioral study to state that the images compressed using defocus cue preserving compression yields better depth perception as compared to standard JPEG compression. We compare images compressed using different schemes against the original image. We collect data from different participants by showing original and compressed images to them. The responses are analyzed using analysis of variance. The analysis shows that the images compressed using defocus cue based compression provides the better perception of the raw image as compared to standard JPEG compressed image. © Springer International Publishing AG 2017.

Behavioral study of defocus cue preserving image compression algorithm for depth perception

Quantification of physiological changes in plants can capture different drought mechanisms and assist in selection of tolerant varieties in a high throughput manner. In this context, an accurate 3D model of plant canopy provides a reliable representation for drought stress characterization in contrast to using 2D images. In this paper, we propose a novel end-to-end pipeline including 3D reconstruction, segmentation and feature extraction, leveraging deep neural networks at various stages, for drought stress study. To overcome the high degree of self-similarities and self-occlusions in plant canopy, prior knowledge of leaf shape based on features from deep siamese network are used to construct an accurate 3D model using structure from motion on wheat plants. The drought stress is characterized with a deep network based feature aggregation. We compare the proposed methodology on several descriptors, and show that the network outperforms conventional methods. © 2017 IEEE.

Proceedings - 2017 IEEE International Conference on Computer Vision Workshops, ICCVW 2017

Drought Stress Classification Using 3D Plant Models

We propose a parametrized walk-based measure for the lack of balance in signed networks inspired by the Katz measure of similarity of two vertices in a network. We show that the performance of the proposed measure is marginally better than a recently proposed walk-based measure of the lack of balance for an undirected version of the real-world signed networks: Epinions, Slashdot and WikiElection. The proposed measure can be used to distinguish signed social networks on the basis of their degree of lack of balance. We also establish that cycles of shorter lengths can predict the sign of an edge in these signed networks better than the longer cycles by using the Katz prediction rule.

JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT

Measuring the balance of signed networks and its application to sign prediction

In movies, film stars portray another identity or obfuscate their identity with the help of silicone/latex masks. Such realistic masks are now easily available and are used for entertainment purposes. However, their usage in criminal activities to deceive law enforcement and automatic face recognition systems is also plausible. Therefore, it is important to guard biometrics systems against such realistic presentation attacks. This paper introduces the first-of-its-kind silicone mask attack database which contains 130 real and attacked videos to facilitate research in developing presentation attack detection algorithms for this challenging scenario. Along with silicone mask, there are several other presentation attack instruments that are explored in literature. The next contribution of this research is a novel multilevel deep dictionary learning-based presentation attack detection algorithm that can discern different kinds of attacks. An efficient greedy layer by layer training approach is formulated to learn the deep dictionaries followed by SVM to classify an input sample as genuine or attacked. Experimental are performed on the proposed SMAD database, some samples with real world silicone mask attacks, and four existing presentation attack databases, namely, replay-Attack, CASIA-FASD, 3DMAD, and UVAD. The results show that the proposed algorithm yields better performance compared with state-ofthe-Art algorithms, in both intra-database and cross-database experiments. © 2017 IEEE.

IEEE Transactions on Information Forensics and Security

Detecting silicone mask-based presentation attack via deep dictionary learning

The Visual Object Tracking challenge VOT2017 is the fifth annual tracker benchmarking activity organized by the VOT initiative. Results of 51 trackers are presented; many are state-of-the-art published at major computer vision conferences or journals in recent years. The evaluation included the standard VOT and other popular methodologies and a new 'real-time' experiment simulating a situation where a tracker processes images as if provided by a continuously running sensor. Performance of the tested trackers typically by far exceeds standard baselines. The source code for most of the trackers is publicly available from the VOT page. The VOT2017 goes beyond its predecessors by (i) improving the VOT public dataset and introducing a separate VOT2017 sequestered dataset, (ii) introducing a realtime tracking experiment and (iii) releasing a redesigned toolkit that supports complex experiments. The dataset, the evaluation kit and the results are publicly available at the challenge website1. © 2017 IEEE.