Human trafficking is a global epidemic affecting millions of people across the planet. Sex trafficking, the dominant form of human trafficking, has seen a significant rise mostly due to the abundance of escort websites, where human traffickers can openly advertise among at-will escort advertisements. In this paper, we take a major step in the automatic detection of advertisements suspected to pertain to human trafficking. We present a novel dataset called Trafficking-10k, with more than 10,000 advertisements annotated for this task. The dataset contains two sources of information per advertisement: text and images. For the accurate detection of trafficking advertisements, we designed and trained a deep multimodal model called the Human Trafficking Deep Network (HTDN).
Cite
@article{arxiv.1705.02735,
title = {Combating Human Trafficking with Deep Multimodal Models},
author = {Edmund Tong and Amir Zadeh and Cara Jones and Louis-Philippe Morency},
journal= {arXiv preprint arXiv:1705.02735},
year = {2017}
}