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dc.contributor | Escuela de Ingenierias Industrial, Informática y Aeroespacial | es_ES |
dc.contributor.author | Blanco Medina, Pablo | |
dc.contributor.author | Fidalgo Fernández, Eduardo | |
dc.contributor.author | Alegre Gutiérrez, Enrique | |
dc.contributor.author | Al Nabki, Mohamed Wesam | |
dc.contributor.editor | Tejado Balsera, Inés | |
dc.contributor.editor | Pérez Hernández, Emiliano | |
dc.contributor.editor | Calderón Godoy, Antonio José | |
dc.contributor.editor | González Pérez, Isaías | |
dc.contributor.editor | Merchán García, Pilar | |
dc.contributor.editor | Lozano Rogado, Jesús | |
dc.contributor.editor | Salamanca Miño, Santiago | |
dc.contributor.editor | Vinagre Jara, Blas M. | |
dc.contributor.other | Ingenieria de Sistemas y Automatica | es_ES |
dc.date | 2018 | |
dc.date.accessioned | 2024-05-06T12:44:21Z | |
dc.date.available | 2024-05-06T12:44:21Z | |
dc.identifier.citation | Blanco Medina, P., Fidalgo Fernández, E., Alegre Gutiérrez, E., & Wesam Al Nabki, M. (2018). Detecting Textual Information in Images from Onion Domains Using Text Spotting. En I. Tejado Balsera, E. Pérez Hernández, A. J. Calderón Godoy, I. González Pérez, P. Merchán García, J. S. Lozano Rogado, S. Salamanca Miño, & B. M. Vinagre Jara (eds.), XXXIX Jornadas de Automática: actas. Badajoz, 5-7 de septiembre de 2018. https://doi.org/10.17979/SPUDC.9788497497565.0975 | es_ES |
dc.identifier.isbn | 978-84-9749-756-5 | es_ES |
dc.identifier.other | 10.17979/SPUDC.9788497497565.0975 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10612/20421 | |
dc.description.abstract | [EN] Due to the efforts of different authorities in the fight against illegal activities in the Tor networks, the traders have developed new ways of circumventing the monitoring tools used to obtain evidence of said activities. In particular, embedding textual content into graphical objects avoids that text analysis, using Natural Language Processing (NLP) algorithms, can be used for watching such onion web contents. In this paper, we present a Text Spotting framework dedicated to detecting and recognizing textual information within images hosted in onion domains. We found that the Connectionist Text Proposal Network and Convolutional Recurrent Neural Network achieve 0.57 F-Measure when running the combined pipeline on a subset of 100 images labeled manually obtained from TOIC dataset. We also identified the parameters that have a critical influence on the Text Spotting results. The proposed technique might support tools to help the authorities in detecting these activities. | es_ES |
dc.language | spa | es_ES |
dc.publisher | Universidad de Extremadura | es_ES |
dc.relation.ispartof | XXXIX Jornadas de Automática: actas. Badajoz, 5-7 de septiembre de 2018 | es_ES |
dc.rights | Attribution-NonCommercial 3.0 Unported | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/3.0/ | * |
dc.subject | Informática | es_ES |
dc.subject.other | Text detection | es_ES |
dc.subject.other | Text recognition | es_ES |
dc.subject.other | Cibercrime | es_ES |
dc.subject.other | Machine learning | es_ES |
dc.subject.other | Tor networks | es_ES |
dc.title | Detecting Textual Information in Images from Onion Domains Using Text Spotting | es_ES |
dc.title.alternative | Detección de información textual en imágenes de dominios de cebolla mediante la localización de texto | es_ES |
dc.type | info:eu-repo/semantics/conferencePaper | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.page.initial | 982 | es_ES |
dc.page.final | 975 | es_ES |
dc.subject.unesco | 3304.05 Sistemas de Reconocimiento de Caracteres | es_ES |
dc.subject.unesco | 1207.03 Cibernética | es_ES |
dc.subject.unesco | 1203.17 Informática | es_ES |
dc.description.project | This research is supported by the INCIBE grant “INCIBEI 2015-27359” corresponding to the “Ayudas para la Excelencia de los Equipos de Investigación avanzada en ciberseguridad” and also by the framework agreement between the University of León and INCIBE (Spanish National Cybersecurity Institute) under Addendum 22. We acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. | es_ES |
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