Artificial Intelligence Toolkit Spots New Child Sexual Abuse Media Online
iCOP toolkit could help police catch abusers early
New artificial intelligence software designed to spot new child sexual abuse media online could help police catch child abusers. The toolkit, described in a paper published in Digital Investigation, automatically detects new child sexual abuse photos and videos in online peer-to-peer networks.
The research behind this technology was conducted in the international research project iCOP â Identifying and Catching Originators in P2P Networks â founded by the European Commission Safer Internet Program by researchers at Lancaster University, the German Research Center for Artificial Intelligence (DFKI), and University College Cork, Ireland.
There are hundreds of searches for child abuse images every second worldwide, resulting in hundreds of thousands of child sexual abuse images and videos being shared every year. The people who produce child sexual abuse media are often abusers themselves â the US National Center for Missing and Exploited Children found that 16 percent of the people who possess such media had directly and physically abused children.
Spotting newly produced media online can give law enforcement agencies the fresh evidence they need to find and prosecute offenders. But the sheer volume of activity on peer-to-peer networks makes manual detection virtually impossible. The new toolkit automatically identifies new or previously unknown child sexual abuse media using artificial intelligence.
âIdentifying new child sexual abuse media is critical because it can indicate recent or ongoing child abuse,â explained Claudia Peersman, lead author of the study from Lancaster University. âAnd because originators of such media can be hands-on abusers, their early detection and apprehension can safeguard their victims from further abuse.â
There are already a number of tools available to help law enforcement agents monitor peer-to-peer networks for child sexual abuse media, but they usually rely on identifying known media. As a result, these tools are unable to assess the thousands of results they retrieve and canât spot new media that appear.
The iCOP toolkit uses artificial intelligence and machine learning to flag new and previously unknown child sexual abuse media. The new approach combines automatic filename and media analysis techniques in an intelligent filtering module. The software can identify new criminal media and distinguish it from other media being shared, such as adult pornography.
The researchers tested iCOP on real-life cases and law enforcement officers trialed the toolkit. It was highly accurate, with a false positive rate of only 7.9% for images and 4.3% for videos. It was also complementary to the systems and workflows they already use. And since the system can reveal who is sharing known child sexual abuse media, and show other files shared by those people, it will be highly relevant and useful to law enforcers.
âWhen I was just starting as a junior researcher interested in computational linguistics, I attended a presentation by an Interpol police officer who was arguing that the academic world should focus more on developing solutions to detect child abuse media online,â said Peersman. âAlthough he clearly acknowledged that there are other crimes that also deserve attention, at one point he said: âYou know those sweet toddler hands with dimple-knuckles? I see them onlineâŠ every day.â From that moment I knew I wanted to do something to help stop this. With iCOP we hope weâre giving police the tools they need to catch child sexual abusers early based on what theyâre sharing online.â
The intelligent scalable media analysis methods in iCOP have been developed by the competence center "Multimedia Analysis and Data Mining" (MADM) of the DFKI research group Knowledge Management in Kaiserslautern under the direction of Christian Schulze, a member of DFKIs Deep Learning Competence Center (DLCC).
Notes for editors
The article is "iCOP: Live forensics to reveal previously unknown criminal media on P2P networks," by Claudia Peersman, Christian Schulze, Awais Rashid, Margaret Brennan, and Carl Fischer (doi:10.1016/j.diin.2016.07.002). It appears in Digital Investigation, volume 18 (2016), published by Elsevier.
Copies of this paper are available to credentialed journalists upon request; please contact Elsevier's Newsroom at firstname.lastname@example.org or +31 20 485 2492.
About Digital Investigation
As the International Journal of Digital Forensics & Incident Response, Digital Investigation covers cutting edge developments in digital forensics and incident response from around the globe. This widely referenced publication helps digital investigators remain current on new technologies, useful tools, relevant research, investigative techniques, and methods for handling security breaches. Practitioners in corporate, criminal, and military settings use this journal to share their knowledge and experiences, including current challenges and lessons learned.
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About German Research Center for Artificial Intelligence GmbH (DFKI)
The German Research Center for Artificial Intelligence, with sites in Kaiserslautern, SaarbrĂŒcken, Bremen and a project office in Berlin, is the leading German research institute in the field of innovative software technology. Within the international scientific community, DFKI ranks among the most recognized "Centers of Excellence" and is currently the biggest research center for Artificial Intelligence and its application, in terms of number of employees and volume of external funds, worldwide.