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.

本研究调查了使用图像识别非法活动的替代技术，特别是探讨了使用同构神经网络的One-Shot和Few-Shot学习这一最新方法，我们的解决方案在小规模数据集上具有很高的准确性，因此得出这样的模型是在Dark Web上定义自动执法机构的一种有前景且廉价的替代方案。

当少数人超过大多数：无监督学习在非法内容识别的应用