The challenges of identifying and classifying child sexual exploitation material: Moving towards a more ecologically valid pilot study with digital forensics analysts

Abstract

Background: When child sexual exploitation material is seized, digital forensics analysts are required to manually process all "unknown" digital material by determining (a) whether a child is present in the image, and (b) whether the image is of an indecent nature (i.e., illegal).

Objective: The aim of the present study was to (a) assess the reliability with which CSEM is classified as being of an indecent nature, and (b) examine in detail the decision-making process by analysts.

Participants and setting: Five analysts from a specialist unit at a UK police force took part in the study.

Methods: Participants coded a set of 100 images in order to (i) determine the presence of a child, (ii) estimate the approximate age of the child, and (iii) establish the level of severity depicted in accordance with the UK's legal classification system. Qualitative interviews were conducted to develop a better understanding of analysts' decision-making during the process of identifying and analyzing child sexual exploitation material.

Results: Inter-rater reliability analyses revealed that the level of agreement among analysts was moderate to good in terms of age estimation, and very good in terms of image classification. Using thematic analysis, three superordinate themes were identified, namely (i) establishing the presence of a child, (ii) ambiguity of context, and (iii) coding within legal parameters.

Conclusions: A number of specific aspects and features were identified to play a key role in analysts' decision-making process which may be used to inform current developments that aim to partially automate this process.

Keywords: Child pornography; Child sexual exploitation material; Internet sexual offending; Online child sexual exploitation and abuse; Sexual offenses.