Computer vision-based CSAM screenin

Abstract

The detection of Child Sexual Abuse Material (CSAM) is a major challenge in digital forensics and child protection, while research in this area is constrained by legal, ethical, and privacy restrictions. This paper presents a modular computer vision pipeline for CSAM risk screening based on three proxy visual signals: face detection, apparent age estimation, and adult-content classification. The system integrates YOLOv12n-face, SwinFace, and a ViT-B/16-based pornography classifier, applying a decision rule that flags an image only when evidence of adult content coexists with facial evidence suggesting a possible minor. In addition, two three-class proxy datasets, APD-2M-3C and NPDI-2K-3C, are introduced to distinguish pornographic images, simple non-pornographic images, and challenging non-pornographic images. Since no real CSAM was used, the evaluation focuses on processing performance and false-positive behavior on CSAM-free datasets. Preliminary results show that the pipeline can efficiently process large-scale image collections, although further validation with controlled law-enforcement datasets is required before operational deployment.