构建首个图文儿童检测数据集，助力内容安全自动化识别。
A Manually Annotated Image-Caption Dataset for Detecting Children in the Wild
- 手工标注1万张图文对，覆盖真实与虚构儿童场景。
- 最佳检测方法准确率仅75.3%，凸显任务挑战性。
- 适合内容审核、AI安全与多模态检测研究者使用。
当前平台与法律对未成年人（18岁以下）相关数字内容的监管要求高于其他内容。面对海量需评估的内容，机器学习自动化工具被广泛用于识别未成年人图像。然而，目前尚无针对多模态环境下未成年人检测方法的公开数据集或基准。为此，我们发布了图像-文本儿童在野外数据集（ICCWD），旨在为儿童检测工具提供基准测试支持。该数据集包含10,000个图像-文本对，均经人工标注以判断图像中是否存在儿童，涵盖真实与虚构人物、部分可见身体等多样场景，丰富度超过以往儿童图像数据集。为验证其价值，我们用该数据集评测了三种检测器，包括一个商用年龄估计系统在图像上的应用。结果表明，儿童检测仍是极具挑战的任务，最佳方法达到75.3%的真正例率。我们期望该数据集能推动各类场景下更优未成年人检测方法的设计。
原文摘要 · Abstract (English)
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.
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