A review of Spotting Child Sexual Exploitation Material using File Names and their Path [Póster]

Abstract

Law Enforcement Agencies (LEAs) fight the pro duction and distribution of Child Sexual Exploitation Material (CSEM) daily. Typically, the LEAs engage in manual analysis of the content stored on seized devices suspected of containing CSEM data. This task is complex and time-consuming because of the number of files and the obfuscation and patterns that sexual offenders might use in the file names. We proposed two Natural Language Processing approaches: f irst, training two text classifiers—one dedicated to analyzing f ilenames and the other to examining absolute paths—and then merging their output into a single output. The second uses only the filename classifier recursively over the absolute path. Moreover, we incorporated in both approaches three novel features to enrich the character n-gram representation before training four machine learning classifiers and two Convolutional Neural Networks (CNN). For CSEM detection, we recommend a CNN model combining both approaches, with an F1-score of 0,988, which was integrated into the tool built in the European Project: Global Response Against Child Exploitation (GRACE).