Color Sift Descriptors to Categorize Illegal Activities in Images of Onion Domains

Abstract

Dark Web, i.e. the portion of the Web whose content is not indexed either accessible by standard web browsers, comprises several darknets. The Onion Router (Tor) is the most famous one, thanks to the anonymity provided to its users, and it results in the creation of domains, or hidden services, which hosts illegal activities. In this work, we explored the possibility of identifying illegal domains on Tor darknet based on its visual content. After crawling and filtering the images of 500 hidden services, we sorted them into five different illegal categories, and we trained a classifier using the Bag of Visual Words (BoVW) model. In this model, SIFT (Scale Invariant Feature Transform) or dense SIFT were used as the descriptors of the images patches to compute the visual words of the BoVW model. However, SIFT only works with gray-scale images; thus the information given by color in an image is not retrieved. To overcome this drawback, in this work we implemented and assessed the performance of three different variants of SIFT descriptors that can be used in color images, namely HSV-SIFT, RGB-SIFT and Opponent-SIFT, in the BoVW model for image classification. The obtained results showed the usefulness of using color-SIFT descriptors instead of SIFT, whereas in our experiments the latter achieved an accuracy of 57.52%, the HSV-SIFT descriptor achieved an accuracy up to 59.44%.

Supervised machine learning for classification mining and ranking of illegal web contents = Aprendizaje automático supervisado para la clasificación, extracción de conocimiento y ordenación de contenidos web ilegales

“…Extractive Text Summarization in Dark Web: A Preliminary Study, International Conference of Applications of Intelligent Systems. Matilla, D., González Castro, V., Fernández Robles, L., Fidalgo, E., and Al-Nabki, M. W. (2018). Color SIFT Descriptors to Categorize Illegal Activities in Images of Onion Domains.…”

“…Indeed, despite the lifespan of some DUTA domains is short, as they might be taken down by some LEA or closed by their hosts, the value of DUTA is preserved. Apart from giving insights into Tor in a specific period, it could be used to research several problems, including, but not limited to, the detection of emerging products in the onion domains (Al-Nabki et al, 2017b), image classification Matilla et al, 2018), text summarization (Joshi et al, 2018), or recognition of onion domains services (Biswas et al, 2017). Therefore, we decided to extend DUTA, and we collected new onion domains between May and July 2017.…”