Identifying Online Child Sexual Texts in Dark Web through Machine Learning and Deep Learning Algorithms
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Predators often use the dark web to discuss and share Child Sexual Abuse Material (CSAM) because the dark web provides a degree of anonymity, making it more difficult for law enforcement to track the criminals involved. In most countries, CSAM is considered as forensic evidence of a crime in progress. Processing, identifying and investigating CSAM is often done manually. This is a time-consuming and emotionally challenging task. In this paper, we propose a novel model based on artificial intelligence algorithms to automatically detect CSA text messages in dark web forums. Our algorithms have achieved impressive results in detecting CSAM in dark web, with a recall rate of 89%, a precision rate of 92.3% and an accuracy rate of 87.6%. Moreover, the algorithms can predict the classification of a post in just 1 microsecond and 0.3 milliseconds on standard laptop capabilities. This makes it possible to integrate our model into social network sites or edge devices to for real-time CSAM detection.
Vuong M. Ngo, Susan Mckeever and Christina Thorpe. 2023. Identifying Online Child Sexual Texts in Dark Web through Machine Learning and Deep Learning Algorithms. In APWG.EU Technical Summit and Researchers Sync-Up (APWG.EU-Tech 2023), pp. 1-6, CEUR Workshop Proceedings. DOI: 10.21427/WFN5-RT72
the Safe Online Initiative of End Violence and the Tech Coalition
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This work is licensed under a Creative Commons Attribution 4.0 International License.
The paper was accepted to publish in the proceedings of the APWG.EU Technical Summit and Researchers Sync-Up 2023 (APWG.EU-Tech-2023)