Organization
Auckland University of Technology
Description
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.
File
Automated Biometric Identification using Dorsal Hand Images and Convolutional Neural Networks.pdf
Link
https://iopscience.iop.org/article/10.1088/1742-6596/1880/1/012014
Tags
Neural networksPerpetrators
Type
Research (peer reviewed)
Year
2021
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Dark Web forum posts
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Dark Web forum posts
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Child Sexual Abuse Material (CSAM)Generative AI
Stella Polaris Summit 2024 Summary
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World Childhood Foundation
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World Childhood Foundation
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InternetPolicyDetectionprosecutionPost-crime effortsCriminal investigation
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Cornell University
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University of Pennsylvania Columbia University
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BS-SC Model: A Novel Method for Predicting Child Abuse Using Borderline-SMOTE Enabled Stacking Classifier
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Intelligence
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Intelligence
Technological University Dublin
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Child Sexual Abuse Material (CSAM)Clustering/Classification
Identifying Online Child Sexual Texts in Dark Web through
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Technological University Dublin
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Preferences on the Dark Web
Technological University Dublin
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Activefence
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Activefence
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Generative AIChild Sexual Abuse Material (CSAM)Artificial intelligence
How AI is being abused to create child sexual abuse imagery
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Internet Watch Foundation
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…det var en känsla av att han befann sig i rummet bredvid
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World Childhood Foundation
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San Jose State University
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Griffith University
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University of Warwick
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Lancaster University
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Artificial intelligenceprosecutionChild Sexual Abuse Material (CSAM)Machine learning
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The Economist Intelligence Unit
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PreventionChild Sexual Abuse Material (CSAM)
Developing machine learning-based models to help identify child abuse and neglect: key ethical challenges and recommended solutions
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Internetrelaterade sexuella övergrepp mot barn
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Riksrevisionen
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PreventionChild Sexual Abuse Material (CSAM)prosecution
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Napier
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Australian Institute of Criminology
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Financial transactionsArtificial intelligenceSupervised learningMachine learningCriminal investigationChild Sexual Abuse Material (CSAM)
Automated Biometric Identification using Dorsal Hand Images and Convolutional Neural Networks
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Auckland University of Technology
Research (peer reviewed)
Neural networksPerpetrators
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Oslo Metropolitan University
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Criminal investigationprosecution
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Humboldt Universitat zu Berlin
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Natural Language ProcessingClustering/Classification
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Report
Child Sexual Abuse Material (CSAM)prosecution
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Singidunum University
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Nalla Malla Engineering College, Galgotias University, Vellore Institute of Technology
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Clustering/ClassificationMachine learningPost-crime effortsChild-focusedPost-traumatic stress (PTS)
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Karolinska Institutet
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Göteborgs Universitet
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Summary Paper on Online Child Sexual Exploitation
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ECPAT
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Curbing the Surge
in Online Child Abuse
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in Online Child Abuse
European Parliament
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Action to End child Sexual Abuse and Exploitation
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Unicef
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Child Sexual Abuse Material (CSAM)Prevention
Brief version: Action to End child Sexual Abuse and Exploitation
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Unicef
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Child Sexual Abuse Material (CSAM)Prevention
An Integrative Review of Historical Technology and Countermeasure Usage Trends in Online Child Sexual Exploitation Material Offender
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University of Edinburgh and George Mason University
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Child Sexual Abuse Material (CSAM)PerpetratorsCriminal investigationPrevention
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Griffeye
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Can Artificial Intelligence Achieve Human-Level Performance? A Pilot Study of Childhood Sexual Abuse Detection in Self-Figure Drawings
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University of Haifa
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Artificial intelligencePerpetratorsCriminal investigationSupervised learningNeural networksMachine learningChild-focusedChild Sexual Abuse Material (CSAM)
Estimation of the Development of Depression and PTSD in Children Exposed to Sexual Abuse and Development of Decision Support Systems by Using Artificial Intelligence
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Inonu University
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Child-focusedPost-traumatic stress (PTS)Post-crime efforts
AI Roundtable 2019 Summary
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World Childhood Foundation
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Artificial Intelligence: combating online sexual abuse of children
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Bracket Foundation
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PreventionNeural networksChild Sexual Abuse Material (CSAM)
Out of the Shadows Whitepaper
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The Economist Intelligence Unit
Report
PreventionChild Sexual Abuse Material (CSAM)
What Works to Prevent Sexual Violence Against Children
What Works to Prevent Sexual Violence Against Children
Together for girls
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Child Sexual Abuse Material (CSAM)PreventionFinancial transactions
Child Online Safety:
Minimizing the Risk of Violence,
Abuse and Exploitation Online.
Child Online Safety:
Minimizing the Risk of Violence,
Abuse and Exploitation Online.
ITU/UNESCO Broadband Commission for Sustainable Development
Report
PreventionPerpetrators
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
Research (peer reviewed)
Artificial intelligencePreventionNeural networks
Therabot: An Adaptive Therapeutic Support Robot
Therabot: An Adaptive Therapeutic Support Robot
Institute of Electrical and Electronics Engineers (IEEE) and Mississippi State University
Research (peer reviewed)
Post-crime effortsChild-focusedRoboticsPost-traumatic stress (PTS)
Offender strategies for engaging children in online sexual activity
Offender strategies for engaging children in online sexual activity
Department of Psychology, University of Gothenburg
Research (peer reviewed)
PreventionChild Sexual Abuse Material (CSAM)
Study on the Effects of New Information Technologies on the Abuse and Exploitation
of Children
Study on the Effects of New Information Technologies on the Abuse and Exploitation
of Children
UNODC
Report
PreventionChild Sexual Abuse Material (CSAM)
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
Research (peer reviewed)
PreventionNeural networks