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Institution

International Institute of Information Technology, Hyderabad

EducationHyderabad, India
About: International Institute of Information Technology, Hyderabad is a education organization based out in Hyderabad, India. It is known for research contribution in the topics: Authentication & Internet security. The organization has 2048 authors who have published 3677 publications receiving 45319 citations. The organization is also known as: IIIT Hyderabad & International Institute of Information Technology (IIIT).


Papers
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Proceedings ArticleDOI
01 Sep 2018
TL;DR: This paper introduces Structured Adversarial Training (StrAT), which generates multiple novel views using depth (or disparity), with the stereo-baseline changing in an increasing order, to improve unsupervised depth estimation for monocular images.
Abstract: The problem of estimating scene-depth from a single image has seen great progress lately. Recent unsupervised methods are based on view-synthesis and learn depth by minimizing photometric reconstruction error. In this paper, we introduce Structured Adversarial Training (StrAT) to this problem. We generate multiple novel views using depth (or disparity), with the stereo-baseline changing in an increasing order. Adversarial training that goes from easy examples to harder ones produces richer losses and better models. The impact of StrAT is shown to exceed traditional data augmentation using random new views. The combination of an adversarial framework, multiview learning, and structured adversarial training produces state-of-the-art performance on unsupervised depth estimation for monocular images. The StrAT framework can benefit several problems that use adversarial training.

50 citations

Proceedings ArticleDOI
TL;DR: LipGAN as discussed by the authors generates realistic talking faces from the translated audio, which can significantly improve the overall user experience for consuming and interacting with multimodal content across languages. But it is not suitable for the task of face-to-face translation.
Abstract: In light of the recent breakthroughs in automatic machine translation systems, we propose a novel approach that we term as "Face-to-Face Translation". As today's digital communication becomes increasingly visual, we argue that there is a need for systems that can automatically translate a video of a person speaking in language A into a target language B with realistic lip synchronization. In this work, we create an automatic pipeline for this problem and demonstrate its impact on multiple real-world applications. First, we build a working speech-to-speech translation system by bringing together multiple existing modules from speech and language. We then move towards "Face-to-Face Translation" by incorporating a novel visual module, LipGAN for generating realistic talking faces from the translated audio. Quantitative evaluation of LipGAN on the standard LRW test set shows that it significantly outperforms existing approaches across all standard metrics. We also subject our Face-to-Face Translation pipeline, to multiple human evaluations and show that it can significantly improve the overall user experience for consuming and interacting with multimodal content across languages. Code, models and demo video are made publicly available. Demo video: this https URL Code and models: this https URL

50 citations

Book ChapterDOI
01 Dec 2016
TL;DR: A notion of period summary is introduced by capturing the periodicity of the patterns in a sequence of transaction-ids to reduce the memory requirements and improve the runtime efficiency considerably over existing approaches.
Abstract: Periodic-frequent patterns are an important class of regularities which exists in a transactional database. A frequent pattern is called periodic-frequent if it appears at regular intervals in a transactional database. In the literature, a model of periodic-frequent patterns was proposed and pattern growth like approaches to extract patterns are being explored. In these approaches, a periodic-frequent pattern tree is built in which a transaction-id list is maintained at each path's tail-node. As the typical size of transactional database is very huge in the modern e-commerce era, extraction of periodic-frequent patterns by maintaining transaction-ids in the tree requires more memory. In this paper, to reduce the memory requirements, we introduced a notion of period summary by capturing the periodicity of the patterns in a sequence of transaction-ids. While building the tree, the period summary of the transactions is computed and stored at the tail-node of the tree instead of the transaction-ids. We have also proposed a merging framework for period summaries for mining periodic-frequent patterns. The performance could be improved significantly as the memory required to store the period summaries is significantly less than the memory required to store the transaction-id list. Experimental results show that the proposed approach reduces the memory consumption significantly and also improves the runtime efficiency considerably over the existing approaches.

50 citations

Posted Content
TL;DR: IDD as discussed by the authors is a dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied, and it consists of 10,004 images, annotated with 34 classes collected from 182 drive sequences on Indian roads.
Abstract: While several datasets for autonomous navigation have become available in recent years, they tend to focus on structured driving environments. This usually corresponds to well-delineated infrastructure such as lanes, a small number of well-defined categories for traffic participants, low variation in object or background appearance and strict adherence to traffic rules. We propose IDD, a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. It consists of 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes. It also reflects label distributions of road scenes significantly different from existing datasets, with most classes displaying greater within-class diversity. Consistent with real driving behaviours, it also identifies new classes such as drivable areas besides the road. We propose a new four-level label hierarchy, which allows varying degrees of complexity and opens up possibilities for new training methods. Our empirical study provides an in-depth analysis of the label characteristics. State-of-the-art methods for semantic segmentation achieve much lower accuracies on our dataset, demonstrating its distinction compared to Cityscapes. Finally, we propose that our dataset is an ideal opportunity for new problems such as domain adaptation, few-shot learning and behaviour prediction in road scenes.

50 citations

Journal ArticleDOI
TL;DR: This paper aims to propose an effective and more secure three-factor remote user authentication scheme for TMIS that provides the user anonymity property and shows that the scheme is secure against various known attacks, including the replay and man-in-the-middle attacks.
Abstract: Recent advanced technology enables the telecare medicine information system (TMIS) for the patients to gain the health monitoring facility at home and also to access medical services over the Internet of mobile networks. Several remote user authentication schemes have been proposed in the literature for TMIS. However, most of them are either insecure against various known attacks or they are inefficient. Recently, Tan proposed an efficient user anonymity preserving three-factor authentication scheme for TMIS. In this paper, we show that though Tan's scheme is efficient, it has several security drawbacks such as (1) it fails to provide proper authentication during the login phase, (2) it fails to provide correct updation of password and biometric of a user during the password and biometric update phase, and (3) it fails to protect against replay attack. In addition, Tan's scheme lacks the formal security analysis and verification. Later, Arshad and Nikooghadam also pointed out some security flaws in Tan's scheme and then presented an improvement on Tan's s scheme. However, we show that Arshad and Nikooghadam's scheme is still insecure against the privileged-insider attack through the stolen smart-card attack, and it also lacks the formal security analysis and verification. In order to withstand those security loopholes found in both Tan's scheme, and Arshad and Nikooghadam's scheme, we aim to propose an effective and more secure three-factor remote user authentication scheme for TMIS. Our scheme provides the user anonymity property. Through the rigorous informal and formal security analysis using random oracle models and the widely-accepted AVISPA (Automated Validation of Internet Security Protocols and Applications) tool, we show that our scheme is secure against various known attacks, including the replay and man-in-the-middle attacks. Furthermore, our scheme is also efficient as compared to other related schemes.

50 citations


Authors

Showing all 2066 results

NameH-indexPapersCitations
Ravi Shankar6667219326
Joakim Nivre6129517203
Aravind K. Joshi5924916417
Ashok Kumar Das562789166
Malcolm F. White5517210762
B. Yegnanarayana5434012861
Ram Bilas Pachori481828140
C. V. Jawahar454799582
Saurabh Garg402066738
Himanshu Thapliyal362013992
Monika Sharma362384412
Ponnurangam Kumaraguru332696849
Abhijit Mitra332407795
Ramanathan Sowdhamini332564458
Helmut Schiessel321173527
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202310
202229
2021373
2020440
2019367
2018364