用结构图替代敏感图像，保护隐私的同时支持儿童安全研究。
CSA-Graphs: A Privacy-Preserving Structural Dataset for Child Sexual Abuse Research
- 以场景图和骨骼图替代原始图像，保留上下文信息
- 联合使用两种图结构可提升分类准确率
- 适合关注儿童安全与隐私保护的视觉算法研究者
儿童性虐待图像（CSAI）分类是计算机视觉领域重要但极具挑战的问题，因法律与伦理限制，公开共享CSAI数据集受到严格约束，阻碍了方法复现与技术进展。本文提出CSA-Graphs，一种隐私保护的结构性数据集。不发布原始图像，而是提供移除显性视觉内容但仍保留上下文信息的结构表示。该数据集包含两类互补的图模态：描述物体关系的场景图与编码人体姿态的骨骼图。实验表明，两类表示均能有效支持CSAI分类，且融合后性能进一步提升。该数据集在遵守法律与伦理前提下，推动了儿童安全相关视觉方法的研究。
原文摘要 · Abstract (English)
Child Sexual Abuse Imagery (CSAI) classification is an important yet challenging problem for computer vision research due to the strict legal and ethical restrictions that prevent the public sharing of CSAI datasets. This limitation hinders reproducibility and slows progress in developing automated methods. In this work, we introduce CSA-Graphs, a privacy-preserving structural dataset. Instead of releasing the original images, we provide structural representations that remove explicit visual content while preserving contextual information. CSA-Graphs includes two complementary graph-based modalities: scene graphs describing object relationships and skeleton graphs encoding human pose. Experiments show that both representations retain useful information for classifying CSAI, and that combining them further improves performance. This dataset enables broader research on computer vision methods for child safety while respecting legal and ethical constraints.
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