Department of Computer Science & Engineering

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IIT Jodhpur

We propose a graph-based deep network for predicting the associations pertaining to field labels and field values in heterogeneous handwritten form images. We consider forms in which the field label comprises printed text and field value can be the handwritten text. Inspired by the relationship predicting capability of the graphical models, we use a Graph Autoencoder to perform the intended field label to field value association in a given form image. To the best of our knowledge, it is the first attempt to perform label-value association in a handwritten form image using a machine learning approach. We have prepared our handwritten form image dataset comprising 300 images from 30 different templates having 10 images per template. Our framework is experimented on different network parameter and has shown promising results.

World University root node

2019 International Conference on Document Analysis and Recognition Workshops, ICDARW 2019

Associating field components in heterogeneous handwritten form images using Graph Autoencoder

Cognitive radio networks (CRNs) consist of different intellectual users with capability of sensing and sharing underutilised spectrum, and they are called as cognitive users (CUs). The spectrum is allocated to licensed users or primary users (PUs), but they do not utilise it completely. To overcome an ever-increasing spectrum demand and utilise the underutilised licensed spectrum, the cognitive radio plays a major role. In this distributed network, communications among CUs become more challenging due to channel heterogeneity, uncontrolled environment, a need of cooperative and accurate sensing, and different security attacks. Thus, this affects and degrades system and CRN performance, which creates an opportunity for a trust management model to manage the CRNs properly. In this paper, we propose an efficient trust management protocol for centralised and distributed CRNs to build a trust-based system over the complete cognitive cycle to protect against security attacks brought by the unreliable individuals. The security analysis and simulation results represent that the proposed protocol can identify malicious behaviour and thus, it enhances reliability of the network in centralised and distributed circumstances. Copyright © 2019 Inderscience Enterprises Ltd.

International Journal of Ad Hoc and Ubiquitous Computing

A reputation-based truthfulness paradigm for multi-hop transmission in cognitive radio networks

In the present world, we are enabled with distinct technology devices and/or systems mostly to reduce human effort in completing various tasks. To fulfill our activities within a reasonable period, we expect reliable communication system(s) to exchange significant data securely. The vehicular cloud computing (VCC) is a system to control vehicle-related data for various computations and this data helps to different vehicle operators straightaway or indirectly. However, a receiver should confirm the correctness of obtained information else it influences in erroneous ways. Recently, Zhong et al. suggested a privacy-preserving authentication model but in this paper, we identify that this scheme cannot withstand against some attacks, i.e., impersonation, modification, plain-text, and man-in-the-middle. Thus, we propose an improved message confirmation system for the VCC to protect various security attacks, e.g., replay, plain-text, impersonation, man-in-the-middle, and modification. Further, we do performance and security analysis of the suggested method. Next, we compare the proposed system with different message verification protocols and the results show that the suggested method is more secure and effective compared to other related communication protocols. © 2018 Elsevier B.V.

Physical Communication

Secure message confirmation scheme based on batch verification in vehicular cloud computing

To maintain user quality in IEEE 802.11 networks could also be a troublesome issue, and conjointly the necessity for Multimedia Applications. To take care of the quality of the connections, these Multimedia applications want fast handoffs between Base Stations (BS). Previous work is based on maintaining a neighbor table by presently associated Access Point (AP) and scan delay. In this paper, we have made an attempt to propose a Neighboring approach where scan delay has been reduced to some zero by exploiting the MH neighbor table. It contains detailed knowledge of all the applications that unit at intervals. The MH neighbor table has been maintained by MH itself and its updated once each 0.5 s interval therefore it ensures the MH neighbor table contains details of exclusions that APs unit extremely neighbor of MH nonetheless as MH context knowledge has been transferred by presently associated AP to exclude that APs that’s at intervals, or we’ll recommend that APs that details have been sent by MH to presently associated AP. The result shows that this approach is better than all existing approaches as a result of it reduces scan delay to some zero and to boot reduces re-association delay, that the general handoff delay is unbelievably low for Multimedia Applications. © 2019, Springer Nature Switzerland AG.

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

A fast handoff technique for wireless mobile networks

Recent proliferation in disruptive technologies has opened up a new horizon for Internet of Vehicles (IoV). The success of IoV highly depends on the robustness of vehicular information system as a dispute among the service providers on data rights or any kind of security violation could disrupt the transport services altogether. In this work we propose a blockchain based novel architecture for vehicle authentication and privacy preservation with seamless access control for IoV. Proposed architecture is decentralized, robust and scalable. Along with privacy preserving authentication and conflict-free access-log maintenance, the proposed BlockAPP protocol also supports an optional traceability feature. Performance evaluation using smart contact over Ethereum Blockchain validates the effectiveness of the proposed architecture. © 2018 IEEE.

2018 IEEE Globecom Workshops, GC Wkshps 2018 - Proceedings

BlockAPP: Using Blockchain for Authentication and Privacy Preservation in IoV

Driven by the growth in ubiquitous sensor networks and IoT, Human Activity Recognition (HAR) has emerged to be an exciting challenge. Popular machine learning approaches proposed in this direction have shown promise with modest performance. Treatment of HAR data in a recurrent form, and subsequent analysis using deep networks such as RNNs/LSTM is largely unexplored. In this work, we treat HAR data stream, generated from a smart home setup, as a series of events and apply LSTM to perform feature extraction. We use the Aruba dataset from the CASAS project [4], where we apply LSTM to extract features and perform annotations. We then apply standard classification techniques to recognize the different activities in the newly annotated data. We observe that, using our method we can achieve annotation accuracies of up to 79.5% which is 13.6% better than other state-of-the-art solutions. © 2019 IEEE.

2019 11th International Conference on Communication Systems and Networks, COMSNETS 2019

Learning and Annotating Activities for Home Automation using LSTM

Authenticated encryption schemes are usually expected to offer confidentiality and authenticity. In case of release of unverified plaintext (RUP), an adversary gets separated access to the decryption and verification functionality, and has more power in breaking the scheme. Andreeva et al. (ASIACRYPT 2014) formalized RUP security using plaintext awareness, informally meaning that the decryption functionality gives no extra power in breaking confidentiality, and INT-RUP security, covering authenticity in case of RUP. We describe a single, unified model, called AERUP security, that ties together these notions: we prove that an authenticated encryption scheme is AERUP secure if and only if it is conventionally secure, plaintext aware, and INT-RUP secure. We next present ANYDAE, a generalization of SUNDAE of Banik et al. (ToSC 2018/3). ANYDAE is a lightweight deterministic scheme that is based on a block cipher with block size n and arbitrary mixing functions that all operate on an n-bit state. It is particularly efficient for short messages, it does not rely on a nonce, and it provides maximal robustness to a lack of secure state. Whereas SUNDAE is not secure under release of unverified plaintext (a fairly simple attack can be mounted in constant time), ANYDAE is. We make handy use of the AERUP security model to prove that ANYDAE achieves both conventional security as RUP security, provided that certain modest conditions on the mixing functions are met. We describe two simple instances, called MONDAE and TUESDAE, that conform to these conditions and that are competitive with SUNDAE, in terms of efficiency and optimality. © 2019, Ruhr-Universitat Bochum. All rights reserved.

IACR Transactions on Symmetric Cryptology

Release of unverified plaintext: Tight unified model and application to ANYDAE

In recent past, there has been a steep increase in the use of online platforms for the search of desired products. Real estate industry is no exception and has started initiating rent/sale of houses through online platforms. In this paper, we propose a deep neural network framework to facilitate automatic search of homes based on their floor plans. The salient features of this framework are that the query can be either an image (existing floor plan) or a sketch through a sketch pad interface. Our proposed framework automatically determines the type of query (image or sketch) and retrieves similar floor plan images from the database. The critical contributions of our proposed approach are: (1) a novel unified floor plan retrieval framework using multimodal query, i.e., an intuitive and convenient sketch query mode as well as a query by example mode ; (2) a conjunction of autoencoder, Cyclic GAN and CNN for the task of domain mapping and floor plan image retrieval. We have reported results of extensive experimentation and comparison with baseline results to establish the effectiveness of our approach. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

International Journal on Document Analysis and Recognition

A novel feature transform framework using deep neural network for multimodal floor plan retrieval

The performance of a biometric system that relies on a single biometric modality (e.g., fingerprints only) is often stymied by various factors such as poor data quality or limited scalability. Multibiometric systems utilize the principle of fusion to combine information from multiple sources in order to improve recognition accuracy whilst addressing some of the limitations of single-biometric systems. The past two decades have witnessed the development of a large number of biometric fusion schemes. This paper presents an overview of biometric fusion with specific focus on three questions: what to fuse, when to fuse, and how to fuse. A comprehensive review of techniques incorporating ancillary information in the biometric recognition pipeline is also presented. In this regard, the following topics are discussed: (i) incorporating data quality in the biometric recognition pipeline; (ii) combining soft biometric attributes with primary biometric identifiers; (iii) utilizing contextual information to improve biometric recognition accuracy; and (iv) performing continuous authentication using ancillary information. In addition, the use of information fusion principles for presentation attack detection and multibiometric cryptosystems is also discussed. Finally, some of the research challenges in biometric fusion are enumerated. The purpose of this article is to provide readers a comprehensive overview of the role of information fusion in biometrics. © 2019 Elsevier B.V.

Fulltext

Information Fusion

A comprehensive overview of biometric fusion

Analysis of stomata density and its configuration based on scanning electron microscopic (SEM) image of a leaf surface, is an effective way to characterize the plant’s behaviour under various environmental stresses (drought, salinity etc.). Existing methods for phenotyping these stomatal traits are often based on manual or semi-automatic labeling and segmentation of SEM images. This is a low-throughput process when large number of SEM images is investigated for statistical analysis. To overcome this limitation, we propose a novel automated pipeline leveraging deep convolutional neural networks for stomata detection and its quantification. The proposed framework shows a superior performance in contrast to the existing stomata detection methods in terms of precision and recall, 0.91 and 0.89 respectively. Furthermore, the morphological traits (i.e. length & width) obtained at stomata quantification step shows a correlation of 0.95 and 0.91 with manually computed traits, resulting in an efficient and high-throughput solution for stomata phenotyping. © Springer Nature Switzerland AG 2019.