File Name Classification Approach to Identify Child Sexual Abuse

Mhd Wesam Al-Nabki, Mhd Wesam Al-Nabki, Eduardo Fidalgo, Eduardo Fidalgo, Enrique Alegre, Enrique Alegre, Rocío Aláiz-Rodríguez, Rocío Aláiz-Rodríguez

2020

Abstract

When Law Enforcement Agencies seize a computer machine from a potential producer or consumer of Child Sexual Exploitation Material (CSEM), they need accurate and time-efficient tools to analyze its files. However, classifying and detecting CSEM by manual inspection is a high time-consuming task, and most of the time, it is unfeasible in the amount of time available for Spanish police using a search warrant. An option for identifying CSEM is to analyze the names of the files stored in the hard disk of the suspect person, looking in the text for patterns related to CSEM. However, due to the particularity of this file names, mainly its length and the use of obfuscated words, current file name classification methods suffer from a low recall rate, which is essential in the context of this problem. This paper presents our ongoing research to identify CSEM through their file names. We evaluate two approaches of short text classification: a proposal based on machine learning classifiers exploring the use of Logistic Regression and Support Vector Machine and an approach using deep learning by adapting two popular Convolutional Neural Network (CNN) models that work on character-level. The presented CNN achieved an average class recall of 0.86 and a recall rate of 0.78 for the CSEM class. The CNN based classifier could be integrated into forensic tools and services that might support Law Enforcement Agencies to identify CSEM without the need to access systematically to the visual content of every file.

Paper Citation

in Harvard Style

Al-Nabki M., Fidalgo E., Alegre E. and Aláiz-Rodríguez R. (2020). File Name Classification Approach to Identify Child Sexual Abuse. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 228-234. DOI: 10.5220/0009154802280234

in Bibtex Style

@conference{icpram20,
author={Mhd Wesam Al-Nabki and Eduardo Fidalgo and Enrique Alegre and Rocío Aláiz-Rodríguez},
title={File Name Classification Approach to Identify Child Sexual Abuse},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={228-234},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009154802280234},
isbn={978-989-758-397-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - File Name Classification Approach to Identify Child Sexual Abuse
SN - 978-989-758-397-1
AU - Al-Nabki M.
AU - Fidalgo E.
AU - Alegre E.
AU - Aláiz-Rodríguez R.
PY - 2020
SP - 228
EP - 234
DO - 10.5220/0009154802280234