New report

Inonu University

The report describes an experiment in which algorithms were able to predict whether a child had PTSD or major depression, with 99.2% accuracy, based solely on data about the person and the assault. This meant that no additional information was required beyond what is usually collected in child sexual abuse cases. The results indicate that it is possible to use AI early after an abuse has occurred to predict whether PTSD or major depression is likely to develop.

Oslo Metropolitan University

The report describes how virtual avatars of children can be used to train police officers to interview children about sexual abuse. Furthermore, the interviews can be used to create synthetic data from interviews with children.

Auckland University of Technology

The researchers are investigating how well AI can be applied to identify people based solely on the back of their hands. They achieve accuracies of over 99.9%, indicating that such tools can be used to identify perpetrators in child sexual abuse material. Similar projects have been conducted in Lancaster University.

San Jose State University

The report examines how biometric (facial) search combined with voice can be used to identify abuse survivors and perpetrators, by searching for matches in other child sexual abuse material videos.

Griffith University

A study of what police officers believe they need to more effectively counter child sexual abuse material. The main training/support mentioned that organisations should provide included: communication, training on different types of perpetrators, and psychological support.

The report describes how computer-generated child abuse material is becoming increasingly common, including through deep fake technology. The author argues that the US law 18 U.S. Code § 2256 should be revised to prohibit computer-generated abuse material as well.

Humboldt Universitat zu Berlin

A report on how deep learning transform models can classify grooming attempts. The authors of the report created a dataset that was then used by Viktor Bowallius and David Eklund in the report Grooming detection of chat segments using transformer models, where an f1 score of 0.98 was achieved.

Singidunum University

The article looks at how AI can analyse activity on mobile screens and audio ports, to detect bullying, porn and sexual harassment. Unlike previous experiments, this AI can see all activity as the user sees it, and not just see input in the form of texts or images that are retrieved from the screen and then processed. The model achieves an average accuracy of 88% when classifying texts, such as classifying sexism and racism. Furthermore, the model achieves 95% accuracy in detecting pornography.

Nalla Malla Engineering College, Galgotias University, Vellore Institute of Technology

The report evaluates how well an AI can detect child sexual abuse via surveillance cameras.

Adhiyamaan College of Engineering

A report on a downloadable AI that analyses browser history to assess the risk that a child may be sexually exploited. The report does not provide concrete figures on how well the tool works.