Rethinking the Detection of Child Sexual Abuse Imagery on the Internet

Abstract

Over the last decade, the illegal distribution of child sexual abuse imagery (CSAI) has transformed alongside the rise of online sharing platforms. In this paper, we present the first longitudinal measurement study of CSAI distribution online and the threat it poses to society's ability to combat child sexual abuse. Our results illustrate that CSAI has grown exponentially-to nearly 1 million detected events per month-exceeding the capabilities of independent clearinghouses and law enforcement to take action. In order to scale CSAI protections moving forward, we discuss techniques for automating detection and response by using recent advancements in machine learning.