Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering

[EN] One of the tasks Law Enforcement Agencies are responsible for is to find evidence of criminal activities in the Darknet. However, visiting thousands of domains to locate visual information containing illicit acts manually requires a considerable amount of time and human resources. To support th...

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Detalhes bibliográficos
Autores: Fidalgo Fernández, Eduardo, Alegre Gutiérrez, Enrique, Fernández Robles, Laura, González Castro, Víctor
Formato: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2019
País:España
Recursos:Universidad de León
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/23155
Acesso em linha:https://hdl.handle.net/10612/23155
Access Level:acceso abierto
Palavra-chave:Informática
Ingeniería de sistemas
Artificial intelligence
Digital investigation
Tor darknet
Cybercrime analysis
Image classification
Bag of visual words
Saliency map
1209.03 Análisis de Datos
1203.04 Inteligencia Artificial
2209.90 Tratamiento Digital. Imágenes
1203.12 Bancos de Datos
3304.05 Sistemas de Reconocimiento de Caracteres
id ES_4801e13d282e20e4ef6acda13ee1e8d5
oai_identifier_str oai:buleria.unileon.es:10612/23155
network_acronym_str ES
network_name_str España
repository_id_str
spelling Classifying suspicious content in tor darknet through Semantic Attention Keypoint FilteringFidalgo Fernández, EduardoAlegre Gutiérrez, EnriqueFernández Robles, LauraGonzález Castro, VíctorInformáticaIngeniería de sistemasArtificial intelligenceDigital investigationTor darknetCybercrime analysisImage classificationBag of visual wordsSaliency map1209.03 Análisis de Datos1203.04 Inteligencia Artificial2209.90 Tratamiento Digital. Imágenes1203.12 Bancos de Datos3304.05 Sistemas de Reconocimiento de Caracteres[EN] One of the tasks Law Enforcement Agencies are responsible for is to find evidence of criminal activities in the Darknet. However, visiting thousands of domains to locate visual information containing illicit acts manually requires a considerable amount of time and human resources. To support this task, in this paper, we explore the automatic classification of images uploaded to Tor darknet. Unfortunately, the foreground objects on such images are not always presented standalone, without background, due to the environmental conditions. To address this challenge on the digital investigation of Tor darknet visual content, we propose to classify automatically only relevant parts of the image combining saliency maps, i.e. to select the regions with the most salient information, with Bag of Visual Words (BoVW). We introduce Semantic Attention Keypoint Filtering (SAKF), a filtering strategy that removes non-significant features at a pixel level that mainly do not belong to the object of interest or foreground. We assessed SAKF on seven publicly available datasets, obtaining from 1.64 to 15.73 points higher accuracies than the method set as the baseline, i.e. BoVW using dense SIFT (Scale-Invariant Feature Transform) descriptors. We also compared SAKF filtering performance against the deep features extracted from two well-known Convolutional Neural Network (CNN) architectures, namely MobileNet and ResNet50. Experimental results reveal the effectiveness of the proposed approach and highlight that the use of automatic image classification could be advantageous to support daily Law Enforcement Agencies investigations on Tor darknet. Graphical abstractSIThis research was 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 Addenda 22 and 01.Instituto Nacional de CiberseguridadElsevierIngenieria de Sistemas y AutomaticaEscuela de Ingenierias Industrial, Informática y Aeroespacial2019info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttps://hdl.handle.net/10612/23155reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Universidad de LeónIngléshttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/231552026-06-24T12:43:27Z
dc.title.none.fl_str_mv Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering
title Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering
spellingShingle Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering
Fidalgo Fernández, Eduardo
Informática
Ingeniería de sistemas
Artificial intelligence
Digital investigation
Tor darknet
Cybercrime analysis
Image classification
Bag of visual words
Saliency map
1209.03 Análisis de Datos
1203.04 Inteligencia Artificial
2209.90 Tratamiento Digital. Imágenes
1203.12 Bancos de Datos
3304.05 Sistemas de Reconocimiento de Caracteres
title_short Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering
title_full Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering
title_fullStr Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering
title_full_unstemmed Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering
title_sort Classifying suspicious content in tor darknet through Semantic Attention Keypoint Filtering
dc.creator.none.fl_str_mv Fidalgo Fernández, Eduardo
Alegre Gutiérrez, Enrique
Fernández Robles, Laura
González Castro, Víctor
author Fidalgo Fernández, Eduardo
author_facet Fidalgo Fernández, Eduardo
Alegre Gutiérrez, Enrique
Fernández Robles, Laura
González Castro, Víctor
author_role author
author2 Alegre Gutiérrez, Enrique
Fernández Robles, Laura
González Castro, Víctor
author2_role author
author
author
dc.contributor.none.fl_str_mv Ingenieria de Sistemas y Automatica
Escuela de Ingenierias Industrial, Informática y Aeroespacial
dc.subject.none.fl_str_mv Informática
Ingeniería de sistemas
Artificial intelligence
Digital investigation
Tor darknet
Cybercrime analysis
Image classification
Bag of visual words
Saliency map
1209.03 Análisis de Datos
1203.04 Inteligencia Artificial
2209.90 Tratamiento Digital. Imágenes
1203.12 Bancos de Datos
3304.05 Sistemas de Reconocimiento de Caracteres
topic Informática
Ingeniería de sistemas
Artificial intelligence
Digital investigation
Tor darknet
Cybercrime analysis
Image classification
Bag of visual words
Saliency map
1209.03 Análisis de Datos
1203.04 Inteligencia Artificial
2209.90 Tratamiento Digital. Imágenes
1203.12 Bancos de Datos
3304.05 Sistemas de Reconocimiento de Caracteres
description [EN] One of the tasks Law Enforcement Agencies are responsible for is to find evidence of criminal activities in the Darknet. However, visiting thousands of domains to locate visual information containing illicit acts manually requires a considerable amount of time and human resources. To support this task, in this paper, we explore the automatic classification of images uploaded to Tor darknet. Unfortunately, the foreground objects on such images are not always presented standalone, without background, due to the environmental conditions. To address this challenge on the digital investigation of Tor darknet visual content, we propose to classify automatically only relevant parts of the image combining saliency maps, i.e. to select the regions with the most salient information, with Bag of Visual Words (BoVW). We introduce Semantic Attention Keypoint Filtering (SAKF), a filtering strategy that removes non-significant features at a pixel level that mainly do not belong to the object of interest or foreground. We assessed SAKF on seven publicly available datasets, obtaining from 1.64 to 15.73 points higher accuracies than the method set as the baseline, i.e. BoVW using dense SIFT (Scale-Invariant Feature Transform) descriptors. We also compared SAKF filtering performance against the deep features extracted from two well-known Convolutional Neural Network (CNN) architectures, namely MobileNet and ResNet50. Experimental results reveal the effectiveness of the proposed approach and highlight that the use of automatic image classification could be advantageous to support daily Law Enforcement Agencies investigations on Tor darknet. Graphical abstract
publishDate 2019
dc.date.none.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10612/23155
url https://hdl.handle.net/10612/23155
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Universidad de León
instname_str Universidad de León
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.812429

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