Institution
Gradiant (Galician Research and Development Center in Advanced Telecommunications)
Nonprofit•Vigo, Spain•
About: Gradiant (Galician Research and Development Center in Advanced Telecommunications) is a nonprofit organization based out in Vigo, Spain. It is known for research contribution in the topics: Desalination & Cloud computing. The organization has 102 authors who have published 169 publications receiving 3318 citations.
Papers
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10 Oct 2014
TL;DR: A comprehensive definition of the fog is offered, comprehending technologies as diverse as cloud, sensor networks, peer-to-peer networks, network virtualisation functions or configuration management techniques.
Abstract: The cloud is migrating to the edge of the network, where routers themselves may become the virtualisation infrastructure, in an evolution labelled as "the fog". However, many other complementary technologies are reaching a high level of maturity. Their interplay may dramatically shift the information and communication technology landscape in the following years, bringing separate technologies into a common ground. This paper offers a comprehensive definition of the fog, comprehending technologies as diverse as cloud, sensor networks, peer-to-peer networks, network virtualisation functions or configuration management techniques. We highlight the main challenges faced by this potentially breakthrough technology amalgamation.
998 citations
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01 Sep 2016
TL;DR: A new database for evaluating face-PAD methods on mobile devices, REPLAY-MOBILE, which includes 1,200 videos corresponding to 40 clients and provides baseline results with state- of-the-art approaches based on image quality analysis and face texture analysis.
Abstract: For face authentication to become widespread on mobile devices, robust countermeasures must be developed for face presentation-attack detection (PAD). Existing databases for evaluating face-PAD methods do not capture the specific characteristics of mobile devices. We introduce a new database, REPLAY-MOBILE, for this purpose.1 This publicly available database includes 1,200 videos corresponding to 40 clients. Besides the genuine videos, the database contains a variety of presentation-attacks. The database also provides three non- overlapping sets for training, validating and testing classifiers for the face-PAD problem. This will help researchers in comparing new approaches to existing algorithms in a standardized fashion. For this purpose, we also provide baseline results with state- of-the-art approaches based on image quality analysis and face texture analysis.
156 citations
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University of Oulu1, Université du Québec2, University of Ouargla3, University of Biskra4, University of the Basque Country5, Hunan University6, Changsha University of Science and Technology7, Indian Institute of Technology Indore8, Gradiant (Galician Research and Development Center in Advanced Telecommunications)9, École Polytechnique Fédérale de Lausanne10, Idiap Research Institute11, Shenzhen University12, Fujitsu13, Northwestern Polytechnical University14, Hong Kong Baptist University15, State University of Campinas16
TL;DR: This competition is to evaluate and compare the generalization performances of mobile face PAD techniques under some real-world variations, including unseen input sensors, presentation attack instruments (PAI) and illumination conditions, on a larger scale OULU-NPU dataset using its standard evaluation protocols and metrics.
Abstract: In recent years, software-based face presentation attack detection (PAD) methods have seen a great progress. However, most existing schemes are not able to generalize well in more realistic conditions. The objective of this competition is to evaluate and compare the generalization performances of mobile face PAD techniques under some real-world variations, including unseen input sensors, presentation attack instruments (PAI) and illumination conditions, on a larger scale OULU-NPU dataset using its standard evaluation protocols and metrics. Thirteen teams from academic and industrial institutions across the world participated in this competition. This time typical liveness detection based on physiological signs of life was totally discarded. Instead, every submitted system relies practically on some sort of feature representation extracted from the face and/or background regions using hand-crafted, learned or hybrid descriptors. Interesting results and findings are presented and discussed in this paper.
150 citations
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TL;DR: A metrological geometric verification of Kinect and Xtion sensors is performed using a standard artifact which consists of five delrin spheres and seven aluminum cubes to confirm that these sensors can be used in many engineering applications when the measurement range is short and accuracy requirements are not very strict.
147 citations
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TL;DR: This paper introduces DeepCAPTCHA, a new and secure CAPTCHA scheme based on adversarial examples, an inherit limitation of the current DL networks, and implements a proof of concept system, which shows that the scheme offers high security and good usability compared with the best previously existing CAPTCHAs.
Abstract: Recent advances in deep learning (DL) allow for solving complex AI problems that used to be considered very hard. While this progress has advanced many fields, it is considered to be bad news for Completely Automated Public Turing tests to tell Computers and Humans Apart (CAPTCHAs), the security of which rests on the hardness of some learning problems. In this paper, we introduce DeepCAPTCHA, a new and secure CAPTCHA scheme based on adversarial examples , an inherit limitation of the current DL networks. These adversarial examples are constructed inputs, either synthesized from scratch or computed by adding a small and specific perturbation called adversarial noise to correctly classified items, causing the targeted DL network to misclassify them. We show that plain adversarial noise is insufficient to achieve secure CAPTCHA schemes, which leads us to introduce immutable adversarial noise —an adversarial noise that is resistant to removal attempts. In this paper, we implement a proof of concept system, and its analysis shows that the scheme offers high security and good usability compared with the best previously existing CAPTCHAs.
140 citations
Authors
Showing all 103 results
Name | H-index | Papers | Citations |
---|---|---|---|
Juan M Sanchez | 52 | 278 | 9422 |
Hung-Hsiang Jonathan Chao | 44 | 170 | 5819 |
Fernando Pérez-González | 31 | 235 | 4389 |
Carmela Troncoso | 29 | 127 | 3383 |
Anurag Gupta | 25 | 110 | 1600 |
James P. McGuire | 25 | 78 | 1955 |
Carlos Mosquera | 23 | 143 | 1863 |
Francisco J. González-Castaño | 21 | 209 | 1847 |
Luis Pérez-Freire | 19 | 31 | 812 |
Felipe Gil-Castineira | 16 | 86 | 742 |
Luis Rodero-Merino | 14 | 25 | 4772 |
Daniel González-Jiménez | 13 | 31 | 660 |
Maximus G. St. John | 13 | 31 | 442 |
Prakash Narayan Govindan | 13 | 32 | 445 |
Kathleen McMillan | 12 | 30 | 758 |