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.

翻译：暗网的匿名性和不可追踪性在为其日益普及提供可能的同时，也成为了众多非法活动的温床。因此，为配合网络安全与执法部门，学术界提出了多种识别和分类非法活动的方法，其中大多数利用文本类暗网市场内容进行识别，而少数方法则使用源自暗网内容的图像。本文探究了这种从图像中识别非法活动的替代性技术。具体而言，我们研究了标签不可知的学习技术，如单样本学习和小样本学习，采用了该领域前沿方法——孪生神经网络。我们的方案能够以较高的准确率处理小规模数据集。特别地，孪生神经网络在20样本实验中对10类数据集达到了90.9%的准确率；这使我们得出结论，此类模型是定义暗网自动化执法机械的一种有前景且成本更低的替代方案。