When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learning

G. Cascavilla, G. Catolino, M. Conti, D. Mellios, D. Tamburri

2023

Abstract

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.

Paper Citation

in Harvard Style

Cascavilla G., Catolino G., Conti M., Mellios D. and Tamburri D. (2023). When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learning. In Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT; ISBN 978-989-758-666-8, SciTePress, pages 324-334. DOI: 10.5220/0012049400003555

in Bibtex Style

@conference{secrypt23,
author={G. Cascavilla and G. Catolino and M. Conti and D. Mellios and D. Tamburri},
title={When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learning},
booktitle={Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT},
year={2023},
pages={324-334},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012049400003555},
isbn={978-989-758-666-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Security and Cryptography - Volume 1: SECRYPT
TI - When the Few Outweigh the Many: Illicit Content Recognition with Few-Shot Learning
SN - 978-989-758-666-8
AU - Cascavilla G.
AU - Catolino G.
AU - Conti M.
AU - Mellios D.
AU - Tamburri D.
PY - 2023
SP - 324
EP - 334
DO - 10.5220/0012049400003555
PB - SciTePress