CSA-Graphs: A Privacy-Preserving Structural Dataset for Child Sexual Abuse Research
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866913016142888960 |
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| author | Caetano, Carlos Laranjeira, Camila Ernesto, Clara Barros, Artur Macedo, João Ribeiro, Leo S. F. Santos, Jefersson A. dos Avila, Sandra |
| author_facet | Caetano, Carlos Laranjeira, Camila Ernesto, Clara Barros, Artur Macedo, João Ribeiro, Leo S. F. Santos, Jefersson A. dos Avila, Sandra |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07132 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CSA-Graphs: A Privacy-Preserving Structural Dataset for Child Sexual Abuse Research Caetano, Carlos Laranjeira, Camila Ernesto, Clara Barros, Artur Macedo, João Ribeiro, Leo S. F. Santos, Jefersson A. dos Avila, Sandra Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | CSA-Graphs: A Privacy-Preserving Structural Dataset for Child Sexual Abuse Research |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.07132 |