Beyond Hash Matching: AI-Based Detection of Previously Unidentified CSAM and its Legal Implications in the EU

Emmanouela Kokolaki, Georgios Samaritakis, Paraskevi Fragopoulou

The detection of previously unidentified Child Sexual Abuse Material (CSAM) remains a significant challenge for law enforcement authorities, online service providers, and child protection organizations, as traditional hash-matching technologies are primarily designed to identify previously verified material. This paper reviews and classifies existing AI-based technologies that could complement hash-based approaches and support the detection, analysis, and prioritization of potentially harmful content. The reviewed approaches are organized into three categories: real-time image and video analysis, image and video classification and tagging, and AI-based behavioral monitoring. A comparative analysis examines the capabilities, limitations, and potential integration of general-purpose computer vision platforms, content moderation services, and specialized CSAM detection tools within existing detection workflows. An experimental evaluation was conducted using Google Cloud Vision AI and Sightengine to investigate sexual-content classification and child-adult age estimation. The results highlight differences in classification taxonomies and outputs across providers, as well as…