Guarding the Guardians: Automated Analysis of Online Child Sexual Abuse
Guarding the Guardians: Automated Analysis of Online Child Sexual Abuse
Criminal investigationNatural Language Processing
2023
Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts
Fine-Tuning Llama 2 Large Language Models for Detecting Online Sexual Predatory Chats and Abusive Texts
Cornell University
Machine learningNatural Language Processing
2023
BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier
BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier
B. S. Abdur Rahman Crescent Institute of Science and Technology
PreventionDetectionClustering/Classification
2023
Applications of artificial intelligence in predicting the risk of child abuse: A literature review
Applications of artificial intelligence in predicting the risk of child abuse: A literature review
King Faisal Specialist Hospital and Research Centre Princess Nora bint Abdul Rahman University Mississippi State University
DetectionChild-focusedNeural networks
2023
Discovering Child Sexual Abuse Material Creator´s Behaviors and
Preferences on the Dark Web
Discovering Child Sexual Abuse Material Creator´s Behaviors and
Preferences on the Dark Web
Technological University Dublin
Child Sexual Abuse Material (CSAM)PerpetratorsClustering/Classification
2023
Developing automated methods to detect and match face and voice biometrics in child sexual abuse videos
Developing automated methods to detect and match face and voice biometrics in child sexual abuse videos
San Jose State University
Child Sexual Abuse Material (CSAM)Perpetrators
2022
Child Sexual Abuse Material Online: The Perspective of Online Investigators on Training and Support
Child Sexual Abuse Material Online: The Perspective of Online Investigators on Training and Support
Griffith University
Child Sexual Abuse Material (CSAM)prosecutionPerpetrators
2022
Child Abuse Risk Prediction and Prevention Framework using AI and Dark Web
Child Abuse Risk Prediction and Prevention Framework using AI and Dark Web
Adhiyamaan College of Engineering
Child Sexual Abuse Material (CSAM)PreventionNeural networks
2022
Applying Artificial Intelligence for Age Estimation in Digital Forensic Investigations
Applying Artificial Intelligence for Age Estimation in Digital Forensic Investigations
University of Warwick
Artificial intelligenceNeural networks
2022
Sperm hunting on optical microscope slides for forensic analysis with deep convolutional networks – a feasibility study
Sperm hunting on optical microscope slides for forensic analysis with deep convolutional networks – a feasibility study
Zurich Institute of Forensic Medicine
Perpetrators
2022
Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions
Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions
Columbia University
EthicsDetectionPrevention
2022
Hand-Based Person Identification Using Global and Part-Aware Deep Feature Representation Learning
Hand-Based Person Identification Using Global and Part-Aware Deep Feature Representation Learning
Lancaster University
Artificial intelligenceprosecutionChild Sexual Abuse Material (CSAM)Machine learning
2022
Automated Biometric Identification using Dorsal Hand Images and Convolutional Neural Networks
Automated Biometric Identification using Dorsal Hand Images and Convolutional Neural Networks
Auckland University of Technology
Neural networksPerpetrators
2021
Multimodal Virtual Avatars for Investigative Interviews with Children
Multimodal Virtual Avatars for Investigative Interviews with Children
Oslo Metropolitan University
Criminal investigationprosecution
2021
Early Detection of Sexual Predators in Chats
Early Detection of Sexual Predators in Chats
Humboldt Universitat zu Berlin
Natural Language ProcessingClustering/Classification
2021
Keeping Children Safe Online With Limited
Resources: Analyzing What is Seen and Heard
Keeping Children Safe Online With Limited
Resources: Analyzing What is Seen and Heard
Singidunum University
PreventionClustering/ClassificationNeural networks
2021
Suspicious activity detection using deep learning in secure assisted living IoT environments
Suspicious activity detection using deep learning in secure assisted living IoT environments
Nalla Malla Engineering College, Galgotias University, Vellore Institute of Technology
Child Sexual Abuse Material (CSAM)Neural networks
2021
Predicting Prolific Live Streaming of Child Sexual Abuse
Predicting Prolific Live Streaming of Child Sexual Abuse
Australian Institute of Criminology
Financial transactionsArtificial intelligenceSupervised learningMachine learningCriminal investigationChild Sexual Abuse Material (CSAM)
2021
Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier
Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier
Uskudar University Medical Faculty, Istanbul, Turkey
Clustering/ClassificationMachine learningPost-crime effortsChild-focusedPost-traumatic stress (PTS)
2021
Estimation of the Development of Depression and PTSD in Children Exposed to Sexual Abuse and Development of Decision Support Systems by Using Artificial Intelligence
Estimation of the Development of Depression and PTSD in Children Exposed to Sexual Abuse and Development of Decision Support Systems by Using Artificial Intelligence
Inonu University
Child-focusedPost-traumatic stress (PTS)Post-crime efforts
2020
Sexuella övergrepp via nätet lika allvarliga som IRL
Sexuella övergrepp via nätet lika allvarliga som IRL
Göteborgs Universitet
Child-focusedChild Sexual Abuse Material (CSAM)Prevention
2020
Illegal Online Sexual Behavior During the COVID-19 Pandemic
Illegal Online Sexual Behavior During the COVID-19 Pandemic
Karolinska Institutet
PreventionPerpetratorsChild Sexual Abuse Material (CSAM)Financial transactions
2020
Can Artificial Intelligence Achieve Human-Level Performance? A Pilot Study of Childhood Sexual Abuse Detection in Self-Figure Drawings
Can Artificial Intelligence Achieve Human-Level Performance? A Pilot Study of Childhood Sexual Abuse Detection in Self-Figure Drawings
University of Haifa
Artificial intelligencePerpetratorsCriminal investigationSupervised learningNeural networksMachine learningChild-focusedChild Sexual Abuse Material (CSAM)
2020
An Integrative Review of Historical Technology and Countermeasure Usage Trends in Online Child Sexual Exploitation Material Offender
An Integrative Review of Historical Technology and Countermeasure Usage Trends in Online Child Sexual Exploitation Material Offender
University of Edinburgh and George Mason University
Child Sexual Abuse Material (CSAM)PerpetratorsCriminal investigationPrevention
2020
A Practitioner Survey Exploring the Value of Forensic Tools, AI, Filtering, & Safer Presentation for Investigating Child Sexual Abuse Material (CSAM)
A Practitioner Survey Exploring the Value of Forensic Tools, AI, Filtering, & Safer Presentation for Investigating Child Sexual Abuse Material (CSAM)
University of New Haven / Digital Forensic Research Workshop
Artificial intelligencePreventionNeural networks
2019
Therabot: An Adaptive Therapeutic Support Robot
Therabot: An Adaptive Therapeutic Support Robot
Institute of Electrical and Electronics Engineers (IEEE) and Mississippi State University
Post-crime effortsChild-focusedRoboticsPost-traumatic stress (PTS)
2018
Offender strategies for engaging children in online sexual activity
Offender strategies for engaging children in online sexual activity
Department of Psychology, University of Gothenburg
PreventionChild Sexual Abuse Material (CSAM)
2017
Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users
Predictive Risk Modelling to Prevent Child Maltreatment and Other Adverse Outcomes for Service Users
University of Queensland
PreventionNeural networks
2015