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...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2019 |
| País: | España |
| Institución: | Universidad de León |
| Repositorio: | BULERIA. Repositorio Institucional de la Universidad de León |
| OAI Identifier: | oai:buleria.unileon.es:10612/23155 |
| Acceso en línea: | https://hdl.handle.net/10612/23155 |
| Access Level: | acceso abierto |
| Palabra clave: | 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 |
| Sumario: | [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 |
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