Document Type

Article

Publication Date

2019

Subject: LCSH

Child sexual abuse--Investigation

Disciplines

Computer Engineering | Computer Sciences | Electrical and Computer Engineering | Forensic Science and Technology | Information Security

Abstract

For those investigating cases of Child Sexual Abuse Material (CSAM), there is the potential harm of experiencing trauma after illicit content exposure over a period of time. Research has shown that those working on such cases can experience psychological distress. As a result, there has been a greater effort to create and implement technologies that reduce exposure to CSAM. However, not much work has explored gathering insight regarding the functionality, effectiveness, accuracy, and importance of digital forensic tools and data science technologies from practitioners who use them. This study focused specifically on examining the value practitioners give to the tools and technologies they utilize to investigate CSAM cases. General findings indicated that implementing filtering technologies is more important than safe-viewing technologies; false positives are a greater concern than false negatives; resources such as time, personnel, and money continue to be a concern; and an improved workflow is highly desirable. Results also showed that practitioners are not well-versed in data science and Artificial Intelligence (AI), which is alarming given that tools already implement these techniques and that practitioners face large amounts of data during investigations. Finally, the data exemplified that practitioners are generally not taking advantage of tools that implement data science techniques, and that the biggest need for them is in automated child nudity detection, age estimation and skin tone detection.

Comments

Dr. Baggili was appointed to the University of New Haven's Elder Family Endowed Chair in 2015,

© 2019 The Author(s). Published by Elsevier Ltd on behalf of DFRWS. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

DOI

10.1016/j.diin.2019.04.005

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Publisher Citation

Sanchez, L., Grajeda, C., Baggili, I., & Hall, C. (2019). A Practitioner Survey Exploring the Value of Forensic Tools, AI, Filtering, & Safer Presentation for Investigating Child Sexual Abuse Material (CSAM). Digital Investigation, 29, S124-S142.

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