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Child Sexual Abuse Material (CSAM) is a serious and growing global problem, intensified by the rapid expansion of digital technologies. Current detection mechanisms are limited in scalability and accuracy. This paper presents an artificial intelligence-based solution for the automated detection and reporting of potential CSAM content. A prototype was developed using image classification models, perceptual hashing, and encrypted logging, offering a scalable and ethical approach to CSAM mitigation.
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Santos et al. (2025) studied this question.