Logo do repositório
 
Publicação

Artificial Intelligence-Driven Cascaded Screening for Age-Based ImageTriage

2026-06-29Dissertação de mestrado Acesso restrito
Ver registo simples
datacite.subject.fosEngenharia e Tecnologia::Outras Engenharias e Tecnologias
dc.contributor.advisorNegrão, Miguel Cerdeira Marreiros
dc.contributor.advisorFrade, Miguel Monteiro de Sousa
dc.contributor.advisorDomingues, Patrício Rodrigues
dc.contributor.authorAmaral, Ricardo Filipe Couceiro
dc.date.accessioned2026-08-05T10:40:15Z
dc.date.available2026-08-05T10:40:15Z
dc.date.issued2026-06-29
dc.description.abstractThe 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.eng
dc.identifier.tid204336988
dc.identifier.urihttp://hdl.handle.net/10400.8/16688
dc.language.isoeng
dc.rights.uriN/A
dc.subjectAge Screening
dc.subjectAI Triage
dc.subjectCascaded Inference
dc.subjectRisk-Aware AI
dc.subjectFace Analy sis
dc.subjectDeep Learning
dc.subjectEthics in AI
dc.titleArtificial Intelligence-Driven Cascaded Screening for Age-Based ImageTriage
dc.typemaster thesis
dspace.entity.typePublication

Ficheiros

Principais
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
ipleiria_thesis_master_03_07_26_c_c_f.pdf
Tamanho:
4.7 MB
Formato:
Adobe Portable Document Format
Ver/Abrir
Licença
A mostrar 1 - 1 de 1
Miniatura indisponível
Nome:
license.txt
Tamanho:
1.32 KB
Formato:
Item-specific license agreed upon to submission
Descrição:
Ver/Abrir

Coleções

ESTG - Mestrado em Cibersegurança e Informática Forense