Department of Computer Science & Engineering

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IIT Jodhpur

In this article, we present a high-performance hardware architecture for Elliptic curve based (authenticated) key agreement protocol “Elliptic Curve Menezes, Qu and Vanstone” (ECMQV) over Binary Edwards Curve (BEC). We begin by analyzing inversion module on a 251-bit binary field. Subsequently, we present Field Programmable Gate Array (FPGA) implementations of the unified formula for computing elliptic curve point addition on BEC in affine and projective coordinates and investigate the relative performance of these two coordinates. Then, we implement the w-coordinate based differential addition formulae suitable for usage in Montgomery ladder. Next, we present a novel hardware architecture of BEC point multiplication using mixed w-coordinates of the Montgomery laddering algorithm and analyze it in terms of resistance to Simple Power Analysis (SPA) attack. In order to improve the performance, the architecture utilizes registers efficiently and uses efficient scheduling mechanisms for the BEC arithmetic implementations. Our implementation results show that the proposed architecture is resistant against SPA attack and yields a better performance when compared to the existing state-of-the-art BEC designs for computing point multiplication (PM). Finally, we present an FPGA design of ECMQV key agreement protocol using BEC defined over GF(2 251 ). The execution of ECMQV protocol takes 66.47μs using 32,479 slices on Virtex-4 FPGA and 52.34μs using 15,988 slices on Virtex-5 FPGA. To the best of our knowledge, this is the first FPGA design of the ECMQV protocol using BEC. © 2018 ACM.

World University root node

ACM Transactions on Reconfigurable Technology and Systems

Reconfigurable hardware architecture for authenticated key agreement protocol over binary edwards curve

The security of modern cryptosystems relies on the secrecy of the keys. Against the expectation that the keys used in cryptographic algorithms are perfectly secure, the keys can get compromised when implemented on physical devices. Because of the computational leakages from the execution of cryptographic algorithms, a variety of side-channel measurements can lead to full breaks of the targeted physical devices. Leakage-resilient cryptography aims at defining leakages in a generic model and designing provably secure primitives to capture side-channel attacks. For this purpose, several re-keying schemes are proposed to prevent encryption scheme from using the same key many times. In this paper, we propose a leakage-resilient authenticated encryption scheme, called Re-keying Code Book (RCB), that is secure against the side-channel attacks by combining with existing re-keying schemes. Our approach is to find efficient composition by combining two independent primitives, authenticated encryption, and re-keying schemes, rather than designing new algorithms. We also give the precise definitions of privacy and authenticity for authenticated encryption in a leakage-resilient model, and then, we provide the security proofs for RCB in a leakage-resilient model. © 2016, Springer Science+Business Media New York.

Journal of Supercomputing

RCB: leakage-resilient authenticated encryption via re-keying

We focus on synthesis of textual description from a given building floor plan image based on the first-person vision perspective. Tasks like symbol spotting, wall and decor segmentation, semantic and perceptual segmentation has been done in the past on floor plans. Here, for the first time, we propose an end-to-end framework for first person vision based textual description synthesis of building floor plans. We demonstrate (qualitative and quantitatively) that the proposed framework gives state of the art performance on challenging, real-world floor plan images. Potential application of this work could be understanding floor plans, stability analysis of buildings, and retrieval. © 2018 IEEE.

Department of Mathematics

Proceedings - 2018 IEEE 14th International Colloquium on Signal Processing and its Application, CSPA 2018

Plan2Text: A framework for describing building floor plan images from first person perspective

In an indoor scenario, the visually impaired do not have the information about the surroundings and finds it difficult to navigate from room to room. The sensor-based solutions are expensive and may not always be comfortable for the end users. In this paper, we focus on the problem of synthesis of textual description from a given floor plan image to assist the visually impaired. The textual description, in addition to a text reading software, can aid the visually impaired person while moving inside a building. In this work, for the first time, we propose an end to end framework (ASYSST) for textual description synthesis from digitized building floor plans. We have introduced a novel Bag of Decor (BoD) feature to learn 5 classes of a room from 1355 samples under a supervised learning paradigm. These learned labels are fed into a description synthesis framework to yield a holistic description of a floor plan image. Experimental analysis of real publicly available floor plan data-set proves the superiority of our framework. © 2018 Association for Computing Machinery.

MAHCI 2018 - Proceedings of the 2018 Workshop on Multimedia for Accessible Human Computer Interface, co-located with MM 2018

ASYSST: A framework for synopsis synthesis empowering visually impaired

We propose a novel motion clustering formulation over spatio-temporal depth images obtained from stereo sequences that segments multiple motion models in the scene in an unsupervised manner. The motion models are obtained at frame rates that compete with the speed of the stereo depth computation. This is possible due to a decoupling framework that first delineates spatial clusters and subsequently assigns motion labels to each of these cluster with analysis of a novel motion graph model. A principled computation of the weights of the motion graph that signifies the relative shear and stretch between possible clusters lends itself to a high fidelity segmentation of the motion models in the scene. The fidelity is vindicated through accuracies reaching 89.61% on KITTI and complex native sequences. © 2018 IEEE.

IEEE Intelligent Vehicles Symposium, Proceedings

Fast Multi Model Motion Segmentation on Road Scenes

While dealing with multi-modal data such as pairs of images and text, though individual samples may demonstrate inherent heterogeneity in their content, they are usually coupled with each other based on some higher-level concepts such as their categories. This shared information can be useful in measuring semantics of samples across modalities in a relative manner. In this paper, we investigate the problem of analysing the degree of specificity in the semantic content of a sample in one modality with respect to semantically similar samples in another modality. Samples that have high similarity with semantically similar samples from another modality are considered to be specific, while others are considered to be relatively ambiguous. To model this property, we propose a novel notion of “cross-specificity”. We present two mechanisms to measure cross-specificity: one based on human judgement and other based on an automated approach. We analyse different aspects of cross-specificity and demonstrate its utility in cross-modal retrieval task. Experiments show that though conceptually simple, it can benefit several existing cross-modal retrieval techniques and provide significant boost in their performance. © 2017, Springer-Verlag London Ltd., part of Springer Nature.

International Journal of Multimedia Information Retrieval

Cross-specificity: modelling data semantics for cross-modal matching and retrieval

Determining the location of a mobile user is central to several crowd-sensing applications. Using a Global Positioning System is not only power-hungry, but also unavailable in many locations. While there has been work on cellular-based localization, we consider an unexplored opportunity to improve location accuracy by combining cellular information across multiple mobile devices located near each other. For instance, this opportunity May arise in the context of public transport units having multiple travelers. Based on theoretical analysis and an extensive experimental study on several public transportation routes in two cities, we show that combining cellular information across nearby phones considerably improves location accuracy. Combining information across phones is especially useful when a phone has to use another phone’s fingerprint database, in a fingerprinting-based localization scheme. Both the median and 90 percentile errors reduce significantly. The location accuracy also improves irrespective of whether we combine information across phones connected to the same or different cellular operators. Sharing information across phones can raise privacy concerns. To address this, we have developed an id-free broadcast mechanism, using audio as a medium, to share information among mobile phones. We show that such communication can work effectively on smartphones, even in real-life, noisy-road conditions. © 2017 ACM.

ACM Transactions on Sensor Networks

CrowdLoc: Cellular fingerprinting for crowds by crowds

Road safety is a major public health issue the world over. Many studies have found that the primary factors responsible for road accidents center on the driver and her/his driving. Hence, there is the need to monitor driver's state and her/his driving, with a view to providing effective feedback. Our proposed demo is of HAMS, a windshield-mounted, smartphone-based system that uses the front camera to monitor the driver and back camera to monitor her/his driving behaviour. The objective of HAMS is to provide ADAS-like functionality with low-cost devices that can be retrofitted onto the large installed base of vehicles that lack specialized and expensive sensors such as LIDAR and RADAR. Our demo would show HAMS in action on an Android smartphone to monitor the state of the driver, specifically such as drowsiness, distraction and gaze, and vehicle ranging, lane detection running on pre-recorded videos from drives. © 2018 Association for Computing Machinery.

Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM

Demo: HAMS: Driver and driving monitoring using a smartphone

Classification using cross-datasets (where a classifier trained using annotated image set A is used to test similar images of set B due to lack of training images in B) is important for many classification problems especially in biomedical imaging. We propose a discriminative autoencoder, useful for addressing the challenge of classification using cross-datasets. Our autoencoder learns an encoder and decoder such that the distances between the representations of the same class is minimized whereas the distances between the representations of different classes are maximized. We derive a fast algorithm to solve the aforementioned problem using the Augmented Lagrangian Alternating Directions Method of Multipliers (ADMM) approach. ADMM is a faster alternative to back-propagation which is used in standard autoencoders. The proposed method outperforms state-of-the-art representation learning tools in terms of classification results in breast cancer related histopathological image set MITOS and AMIDA and some of the benchmark image datasets. © 2018 IEEE.

Proceedings - International Conference on Image Processing, ICIP

Discriminative Autoencoder

Automatic techniques to recognize and evaluate digital logic circuits are more efficient and require less human intervention, as compared to, traditional pen and paper methods. In this paper, we propose LEONARDO (Logic Expression fOrmatioN And eRror Detection framewOrk), a hierarchical approach to recognize boolean expression from hand drawn digital logic gate diagram. The key contributions in the proposed approach are: (i) a novel hierarchical framework to synthesize boolean expression from a hand drawn logic circuit diagram; and (ii) identification of anomalies in drawing. Extensive experimentation was performed through qualitative and quantitative analysis. Results were also compared with existing techniques proposed on the similar problem. Upon experimentation and analysis, our system proved to be more robust to user variability in design and yielded an accuracy of 95.2 %, which is a 4 % gain over others. © Springer Nature Singapore Pte Ltd. 2018.