Making Sense of Darknet Markets: Automatic Inference of Semantic Classifications from Unconventional Multimedia Datasets

Darknet Markets are a hotbed of illicit trade and are difficult for law enforcement to monitor and analyze. Topic Modeling has been a popular method to semantically analyze market listings, but lacks the ability to infer the information-rich visual semantics of images embedded within these listings. In this paper we present a relatively fast method using unsupervised and self-supervised machine learning methods to infer image semantics from large, unstructured multimedia corpora, and demonstrate how it may aid analysts in investigating the content of Darknet Markets.