When the Few Outweigh the Many: Illicit Content Recognition with
  Few-Shot Learning
G. Cascavilla G. Catolino M. Conti D. Mellios D. A. Tamburri
The anonymity and untraceability benefits of the Dark web account for the
exponentially-increased potential of its popularity while creating a suitable
womb for many illicit activities, to date. Hence, in collaboration with
cybersecurity and law enforcement agencies, research has provided approaches
for recognizing and classifying illicit activities with most exploiting textual
dark web markets' content recognition; few such approaches use images that
originated from dark web content. This paper investigates this alternative
technique for recognizing illegal activities from images. In particular, we
investigate label-agnostic learning techniques like One-Shot and Few-Shot
learning featuring the use Siamese neural networks, a state-of-the-art approach
in the field. Our solution manages to handle small-scale datasets with
promising accuracy. In particular, Siamese neural networks reach 90.9% on
20-Shot experiments over a 10-class dataset; this leads us to conclude that
such models are a promising and cheaper alternative to the definition of
automated law-enforcing machinery over the dark web.