Authors: Laura Sanchez (University of New Haven), Cinthya Grajeda Mendez (University of New Haven), Ibrahim Baggili (University of New Haven), Frank Breitinger (University of New Haven)



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 spe- cifically 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.