Illegal activity categorisation in DarkNet based on image classification using CREIC method

Abstract

The TOR Project allows the publication of content anonymously, which cause the proliferation of illegal material whose authorship is almost impossible to identify. In this paper, we present and make publicly available TOIC (TOr Image Categories), an image dataset which comprises five different illegal classes based on crawled TOR addresses. To classify those images we used Edge-SIFT features jointly with dense SIFT descriptors obtained from an “edge image” calculated with the Compass Operator. We demonstrate how a Bag of Visual Words model trained with the early fusion of dense SIFT and Edge-SIFT features can create an efficient model to detect and categorise illegal content in TOR network. Then, we estimated the radius for a complete dataset before the Edge-SIFT calculation, and we demonstrate that the classification performance is higher when the most salient edge information is extracted from the edges. We tested our proposal in both TOIC and in the public dataset Butterflies to prove the consistency of the method, obtaining an accuracy increase of 2.32 and 7.00 points respectively. We obtained with the Ideal Radius Selection an accuracy of 92.49% on TOIC dataset which makes this approach an attractive tool to detect and categorise illegal content in TOR network.