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

arXiv: https://arxiv.org/abs/2506.10117 · HTML: https://mlsanity.com/paper/2506.10117/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 45 references that are in the map.
- First posted on arXiv 11 Jun 2025.

## Builds on (most cited first, 9 of 9)

- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Age Progression/Regression by Conditional Adversarial Autoencoder](https://mlsanity.com/paper/1702.08423/) (2017)
- [Poisoning Web-Scale Training Datasets is Practical](https://mlsanity.com/paper/2302.10149/) (2023)
- [MiVOLO: Multi-input Transformer for Age and Gender Estimation](https://mlsanity.com/paper/2307.04616/) (2023)
- [Seeing without Looking: Analysis Pipeline for Child Sexual Abuse Datasets](https://mlsanity.com/paper/2204.14110/) (2022)
- [Adversarial Detection Avoidance Attacks: Evaluating the robustness of perceptual hashing-based client-side scanning](https://mlsanity.com/paper/2106.09820/) (2021)
- [Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark](https://mlsanity.com/paper/2304.11685/) (2023)
- [Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition](https://mlsanity.com/paper/2301.05776/) (2023)

## Built on by (most cited first, 2 of 2)

- ["Unlimited Realm of Exploration and Experimentation": Methods and Motivations of AI-Generated Sexual Content Creators](https://mlsanity.com/paper/2601.21028/) (2026)
- [A Privacy by Design Framework for Large Language Model-Based Applications for Children](https://mlsanity.com/paper/2602.17418/) (2026)

## Abstract (arXiv)

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.