The research team proposed a CSAM detection intelligence system. The system uses a manually labelled dataset to train, evaluate and select an efficient CSAM classification model. By identifying CSAM creators and victims through CSAM posts on the dark web, we proceed to analyze the material with a classifier, visualizing and uncovering information concerning the behaviors of CSAM creators and victims.
World Childhood Foundation
Bracket Foundation and UNICRI Centre for AI and Robotics
BI Norwegian Business School, Norwegian University of Science and Technology
University of Pennsylvania Columbia University
B. S. Abdur Rahman Crescent Institute of Science and Technology
King Faisal Specialist Hospital and Research Centre Princess Nora bint Abdul Rahman University Mississippi State University
Technological University Dublin
Technological University Dublin
Technological University Dublin
Zurich Institute of Forensic Medicine
Adhiyamaan College of Engineering
The Economist Intelligence Unit
Australian Institute of Criminology
Auckland University of Technology
Humboldt Universitat zu Berlin
Nalla Malla Engineering College, Galgotias University, Vellore Institute of Technology
Uskudar University Medical Faculty, Istanbul, Turkey
University of Edinburgh and George Mason University
The Economist Intelligence Unit
ITU/UNESCO Broadband Commission for Sustainable Development
University of New Haven / Digital Forensic Research Workshop
Institute of Electrical and Electronics Engineers (IEEE) and Mississippi State University
Department of Psychology, University of Gothenburg