Abstract
The distribution of child sexual abuse imagery (CSAI) is an ever-growing concern of our modern world; children who suffered from this heinous crime are revictimized, and the growing amount of illegal imagery distributed overwhelms law enforcement agents (LEAs) with the manual labor of categorization. To ease this burden researchers have explored methods for automating data triage and detection of CSAI, but the sensitive nature of the data imposes restricted access and minimal interaction between real data and learning algorithms, avoiding leaks at all costs. In observing how these restrictions have shaped the literature we formalize a definition of "Proxy Tasks", i.e., the substitute tasks used for training models for CSAI without making use of CSA data. Under this new terminology we review current literature and present a protocol for making conscious use of Proxy Tasks together with consistent input from LEAs to design better automation in this field. Finally, we apply this protocol to study -- for the first time -- the task of Few-shot Indoor Scene Classification on CSAI, showing a final model that achieves promising results on a real-world CSAI dataset whilst having no weights actually trained on sensitive data.
Abstract (translated)
儿童色情虐待图像（CSAI）的传播是现代世界日益增长的关注点；遭受这种罪行侵害的孩子们再次受到伤害，而不断增加的非法图像数量则令执法人员不堪重负，不得不进行大量手动分类工作。为减轻这一负担，研究人员探索了自动化数据筛选和检测CSAI的方法，但由于这类数据的高度敏感性，实际数据与学习算法之间的接触被严格限制，以避免任何可能的数据泄露风险。观察这些限制如何影响相关文献的发展后，我们正式提出了“替代任务”（Proxy Tasks）的概念，即在不使用CSA数据的情况下训练模型的替代方法。在此新术语下，我们将回顾现有文献，并提出一种新的协议，在充分考虑执法人员的意见的同时，有意识地利用替代任务来设计更好的自动化工具。最后，我们将这一协议应用于首次研究的任务：基于CSAI的少量样本室内场景分类，展示了一个在现实世界的CSAI数据集上表现良好、但未使用敏感数据进行训练的最终模型。
URL
https://arxiv.org/abs/2505.06621
