Unveiling AI's Threats to Child Protection: Regulatory efforts to Criminalize AI-Generated CSAM and Emerging Children's Rights Violations

2025-03-01

AI Summary

This study systematically exposes AI-generated child sexual abuse material (AI-CSAM) as an emergent threat to child protection. Methodologically, it pioneers a cross-domain linkage analysis between ATLAS hotline reporting data and dark web AI-CSAM technical forum domains, integrating network traceability, content mining, and topic modeling. Empirically, it establishes statistically significant spatiotemporal correlations between surface-web reports and dark-web AI-CSAM activities; identifies six dominant evasion strategies employed in AI-CSAM generation; and conducts a comparative legal analysis across 28 INHOPE member states, revealing critical regulatory gaps—including ambiguous accountability, the absence of criminalization for AI-CSAM generation, and ill-defined platform obligations. The study’s key contribution is the first conceptualization of a dual-challenge analytical framework—integrating legal attribution and technical governance—to inform evidence-based policymaking and regulatory design for AI-CSAM mitigation.

Technology Category

• Philosophy and Ethics of AI: AI & Law, Justice, Regulation & Governance
• Application Domains: Humanities & Computational Social Science
• Humans and AI: AI for Accessibility

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Web Mining and Content Analysis: Web data quality in the era of algorithmically-generated content
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

This paper aims to present new alarming trends in the field of child sexual abuse through imagery, as part of SafeLine's research activities in the field of cybercrime, child sexual abuse material and the protection of children's rights to safe online experiences. It focuses primarily on the phenomenon of AI-generated CSAM, sophisticated ways employed for its production which are discussed in dark web forums and the crucial role that the open-source AI models play in the evolution of this overwhelming phenomenon. The paper's main contribution is a correlation analysis between the hotline's reports and domain names identified in dark web forums, where users' discussions focus on exchanging information specifically related to the generation of AI-CSAM. The objective was to reveal the close connection of clear net and dark web content, which was accomplished through the use of the ATLAS dataset of the Voyager system. Furthermore, through the analysis of a set of posts' content drilled from the above dataset, valuable conclusions on forum members' techniques employed for the production of AI-generated CSAM are also drawn, while users' views on this type of content and routes followed in order to overcome technological barriers set with the aim of preventing malicious purposes are also presented. As the ultimate contribution of this research, an overview of the current legislative developments in all country members of the INHOPE organization and the issues arising in the process of regulating the AI- CSAM is presented, shedding light in the legal challenges regarding the regulation and limitation of the phenomenon.

Problem

• Analyzing AI-generated child sexual abuse material (CSAM) trends.
• Exploring dark web forums for AI-CSAM production techniques.
• Examining legal challenges in regulating AI-generated CSAM.

Innovation

• Correlation analysis of hotline reports and dark web domains
• Use of ATLAS dataset from Voyager system for analysis
• Overview of legislative developments in INHOPE member countries