Few Images, Many Insights: Illicit Content Detection Using a Limited Number of Images

The anonymity and untraceability benefits of the dark web increased its popularity exponentially. The cost of these technical benefits is that such anonymity has created a suitable womb for illicit activity. Hence—in collaboration with cybersecurity practitioners and law-enforcement agencies—the research community provided approaches for recognizing and classifying illicit activities. Most of these approaches exploit textual content from dark web markets, whereas few used images that originated from them. This paper investigates alternative techniques for recognizing illegal activities from images. The significant contributions of our work are threefold: (a) we investigate label-agnostic learning techniques like One-Shot and Few-Shot learning that use Siamese Neural Networks (SNNs). Our approach manages to handle small-scale datasets with promising accuracy. In particular, the Siamese neural network approach reaches 90.9-Shot experiments over a 10-class dataset. (b) this study's satisfactory findings facilitate the creation of potent tools to assist authorities in identifying illicit content on the web. Moreover, our proof-of-concept approach demonstrated the ability to recognize illegal images using a limited number of files, reducing the time constraint in collecting illegal images. (c) we provide a complete labelled dataset of 3570 images from 55 different categories from dark web markets that can be used for future research activities.