Combating Human Trafficking with Deep Multimodal Models
This work addresses the global issue of human trafficking by providing a tool to identify suspicious ads, potentially aiding law enforcement and anti-trafficking efforts.
The paper tackles the problem of automatically detecting human trafficking advertisements on escort websites by introducing a novel dataset, Trafficking-10k, with over 10,000 annotated ads, and designing a deep multimodal model called HTDN for accurate detection.
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).