A Manually Annotated Image-Caption Dataset for Detecting Children in the Wild

TL;DR

This paper introduces the ICCWD, a comprehensive image-caption dataset for benchmarking child detection methods in diverse scenarios, highlighting the challenges and potential for improving automated minor detection tools.

Contribution

We created and released the ICCWD dataset, the first of its kind, to facilitate benchmarking of multi-modal child detection methods in varied contexts.

Findings

Child detection remains challenging with a best true positive rate of 75.3%.

The dataset enables evaluation of different detection approaches.

Commercial age estimation systems show limited accuracy in this task.

Abstract

Platforms and the law regulate digital content depicting minors (defined as individuals under 18 years of age) differently from other types of content. Given the sheer amount of content that needs to be assessed, machine learning-based automation tools are commonly used to detect content depicting minors. To our knowledge, no dataset or benchmark currently exists for detecting these identification methods in a multi-modal environment. To fill this gap, we release the Image-Caption Children in the Wild Dataset (ICCWD), an image-caption dataset aimed at benchmarking tools that detect depictions of minors. Our dataset is richer than previous child image datasets, containing images of children in a variety of contexts, including fictional depictions and partially visible bodies. ICCWD contains 10,000 image-caption pairs manually labeled to indicate the presence or absence of a child in the image. To demonstrate the possible utility of our dataset, we use it to benchmark three different detectors, including a commercial age estimation system applied to images. Our results suggest that child detection is a challenging task, with the best method achieving a 75.3% true positive rate. We hope the release of our dataset will aid in the design of better minor detection methods in a wide range of scenarios.