Detecting sexually explicit content in the context of the child sexual abuse materials (CSAM): end-to-end classifiers and region-based networks

• URL: http://arxiv.org/abs/2406.14131v1
• Date: Thu, 20 Jun 2024 09:21:08 GMT
• Title: Detecting sexually explicit content in the context of the child sexual abuse materials (CSAM): end-to-end classifiers and region-based networks
• Authors: Weronika Gutfeter, Joanna Gajewska, Andrzej Pacut,
• Abstract summary: Child sexual abuse materials (CSAM) pose a significant threat to the safety and well-being of children worldwide. This study presents methods for classifying sexually explicit content, which plays a crucial role in the automated CSAM detection system.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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