Cribado de CSAM basado en visión artificial
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13766Palabras clave:
Aprendizaje automático, Criminalidad, Procesamiento de imagenResumen
La detección de material de abuso sexual infantil (CSAM) es un desafío relevante en informática forense y protección de menores, cuya investigación está limitada por restricciones legales, éticas y de privacidad. Este trabajo presenta un pipeline modular de visión artificial para el cribado de riesgo de CSAM basado en tres señales visuales proxy: detección facial, estimación de edad aparente y clasificación de contenido adulto. El sistema integra YOLOv12n-face, SwinFace y ViT-B/16, aplicando una regla de decisión que marca una imagen ´cuando coinciden evidencia de contenido adulto y presencia de menores. Además, se proponen dos datasets proxy de tres clases, APD-2M-3C y NPDI-2K-3C, orientados a diferenciar imágenes pornográficas, no pornográficas sencillas y no pornográficas difíciles. Al no utilizar CSAM real, la evaluación se centra en el rendimiento temporal y los falsos positivos sobre datasets sin CSAM. Los resultados preliminares muestran una capacidad eficiente para procesar grandes volúmenes de imágenes, aunque se requiere validación adicional antes de un uso operativo.
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Derechos de autor 2026 Pablo Morais, Eduardo Fidalgo, Víctor González-Castro, Waqar Tanveer, Milad Mirjalili, Enrique Alegre
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-CompartirIgual 4.0.
