Publications

@article{fidalgo_illegal_2018,

title = {Illegal activity categorisation in DarkNet based on image classification using CREIC method},

author = {Eduardo Fidalgo and Enrique Alegre and Victor González-Castro and Laura Fernández-Robles},

url = {https://link.springer.com/chapter/10.1007/978-3-319-67180-2_58},

year  = {2018},

date = {2018-01-01},

journal = {International Joint Conference SOCO’17-CISIS’17-ICEUTE’17 León, Spain, September 6–8, 2017, Proceeding 12},

pages = {600–609},

abstract = {This paper introduces TOIC (TOr Image Categories), a dataset of illegal images from the TOR network, and presents a method to classify them using a combination of Edge-SIFT and dense SIFT descriptors. These features are extracted from edge images created with the Compass Operator. The method employs a Bag of Visual Words model that fuses these descriptors early in the process to effectively detect and categorize illegal content. By selecting the optimal radius before calculating Edge-SIFT, the approach improves classification performance, achieving an accuracy of 92.49% on the TOIC dataset, and showing increased accuracy in tests on both TOIC and the Butterflies dataset. The method offers an efficient tool for identifying illegal content in the TOR network.},

note = {Publisher: Springer International Publishing},

keywords = {Bag of Visual Words, Edge-SIFT descriptors, Image classification, support vector machine, TOR},

pubstate = {published},

tppubtype = {article}

}