A machine learning pipeline for aiding school identification from child trafficking images

TL;DR

This paper presents a machine learning pipeline designed to assist law enforcement in identifying schools from images of children in uniforms, aiming to speed up child trafficking investigations.

Contribution

It introduces a novel two-model pipeline for detecting school uniforms and their attributes in images, automating a labor-intensive process.

Findings

Successfully developed models for uniform detection and attribute classification.

Demonstrated potential to reduce manual effort in school identification.

Validated the pipeline with real-world data from law enforcement collaborations.

Abstract

Child trafficking in a serious problem around the world. Every year there are more than 4 million victims of child trafficking around the world, many of them for the purposes of child sexual exploitation. In collaboration with UK Police and a non-profit focused on child abuse prevention, Global Emancipation Network, we developed a proof-of-concept machine learning pipeline to aid the identification of children from intercepted images. In this work, we focus on images that contain children wearing school uniforms to identify the school of origin. In the absence of a machine learning pipeline, this hugely time consuming and labor intensive task is manually conducted by law enforcement personnel. Thus, by automating aspects of the school identification process, we hope to significantly impact the speed of this portion of child identification. Our proposed pipeline consists of two machine learning models: i) to identify whether an image of a child contains a school uniform in it, and ii) identification of attributes of different school uniform items (such as color/texture of shirts, sweaters, blazers etc.). We describe the data collection, labeling, model development and validation process, along with strategies for efficient searching of schools using the model predictions.