The article explores how facial recognition systems using machine learning can flag material depicting victims or criminals known by law enforcement. The system can also filter and group images that belong to the same case, which makes police officers’ work of going through child sexual abuse material (CSAM) more efficient as they do not need to jump in blindly without knowledge of what could be found or if there are any linking factors. Facial recognition systems have improved significantly in the past few years, especially when applied in uncontrolled circumstances, for example when a person’s face is seen from the side or in motion. Moreover, the systems have also become better at identifying and matching faces of children at different ages, which was almost impossible for the technology a few years ago. Today, systems designed specifically for CSAM exist and their impact has been transformative for the police forces embracing them.
BI Norwegian Business School, Norwegian University of Science and Technology
Technological University Dublin
Technological University Dublin
Technological University Dublin
Zurich Institute of Forensic Medicine
Adhiyamaan College of Engineering
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
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