  Child sexual abuse materials (CSAM) pose a significant threat to the safety
and well-being of children worldwide. Detecting and preventing the distribution
of such materials is a critical task for law enforcement agencies and
technology companies. As content moderation is often manual, developing an
automated detection system can help reduce human reviewers' exposure to
potentially harmful images and accelerate the process of counteracting. This
study presents methods for classifying sexually explicit content, which plays a
crucial role in the automated CSAM detection system. Several approaches are
explored to solve the task: an end-to-end classifier, a classifier with person
detection and a private body parts detector. All proposed methods are tested on
the images obtained from the online tool for reporting illicit content. Due to
legal constraints, access to the data is limited, and all algorithms are
executed remotely on the isolated server. The end-to-end classifier yields the
most promising results, with an accuracy of 90.17%, after augmenting the
training set with the additional neutral samples and adult pornography. While
detection-based methods may not achieve higher accuracy rates and cannot serve
as a final classifier on their own, their inclusion in the system can be
beneficial. Human body-oriented approaches generate results that are easier to
interpret, and obtaining more interpretable results is essential when analyzing
models that are trained without direct access to data.
