Detecting child sexual abuse material: A comprehensive survey
Abstract
Victims of child sexual abuse suffer from physical, psychological, and emotional trauma. The detection and deletion of illicit online child sexual abuse material (CSAM) helps in reducing and even stopping the continuous re-victimization of children. Furthermore, automatic detection may also support legal authorities to search for and review the masses of suspected CSAM. Due to tech-savvy offenders and technological advances, continuous efforts in keeping up with current developments are crucial and need to be considered in the implementation of detection algorithms. The present research provides a comprehensive synthesis and an interpretation of the current research accomplishments and challenges in the CSAM detection domain, explicitly considering the dimensions of policy and legal framework, distribution channels, and detection applications and implementations. Among other aspects, it reveals and aggregates knowledge related to image hash database, keywords, web-crawler, detection based on filenames and metadata, and visual detection. The findings suggest that CSAM detection applications yield the best results if multiple approaches are used in combination, such as deep-learning algorithms with multi-modal image or video descriptors merged together. Deep-learning techniques were shown to outperform other detection methods for unknown CSAM.
