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Artificial Intelligence-Driven Cascaded Screening for Age-Based ImageTriage
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Resumo(s)
The exponential growth of digital visual content associated with Child Sexual Abuse
Material (CSAM) has transformed forensic investigations into large-scale triage prob
lems, particularly in cases involving the identification of underage individuals. Con
ventional age classification systems, designed under closed-set assumptions, are fun
damentallyinadequateforthissetting: theyignoreuncertainty, produceoverconfident
predictions, and fail to meet the safety requirements of forensic decision-making.
This work introduces a risk-aware age screening framework that separates internal
uncertainty modelling from final binary classification. The Primary Screening Model
(PSM) applies a calibrated dual-threshold policy to identify high-confidence and am
biguous cases. PSM-indeterminate cases are then routed to the Secondary Refinement
Model (SRM), whichproduces a binary refinement decision. This design ensures that
the complete system produces a final binary screening output for detected faces, elim
inating model-level indeterminacy entirely. Outputs classified as POSSIBLE_MINOR
are subject to mandatory humanconfirmationpriortoanylegallyconsequentialaction
not because the pipeline is unresolved, but because the ethical and legal weight of de
cisions involving potential child-age class demands human accountability at the point
of consequence.
The proposed framework integrates probabilistic calibration, risk-constrained
threshold selection, explainability via Gradient-weighted Class Activation Mapping
(Grad-CAM), and cryptographic traceability mechanisms, ensuring both technical
robustness and forensic admissibility.
By unifying calibrated uncertainty modelling, PSM-level routing, cascaded refine
ment, and forensic operational constraints, this work moves beyond traditional age
estimation and establishes a practical, scalable, and defensible approach to real-world
forensic triage.
Descrição
Palavras-chave
Age Screening AI Triage Cascaded Inference Risk-Aware AI Face Analy sis Deep Learning Ethics in AI