| Texto completo | |
| Autor(es): |
de Souza, Leonardo Mendes
;
Guido, Rodrigo Capobianco
;
Contreras, Rodrigo Colnago
;
Viana, Monique Simplicio
;
Bongarti, Marcelo Adriano dos Santos
Número total de Autores: 5
|
| Tipo de documento: | Artigo Científico |
| Fonte: | SENSORS; v. 25, n. 15, p. 26-pg., 2025-08-05. |
| Resumo | |
Voice biometric systems play a critical role in numerous security applications, including electronic device authentication, banking transaction verification, and confidential communications. Despite their widespread utility, these systems are increasingly targeted by sophisticated spoofing attacks that leverage advanced artificial intelligence techniques to generate realistic synthetic speech. Addressing the vulnerabilities inherent to voice-based authentication systems has thus become both urgent and essential. This study proposes a novel experimental analysis that extensively explores various dimensionality reduction strategies in conjunction with supervised machine learning models to effectively identify spoofed voice signals. Our framework involves extracting multicepstral features followed by the application of diverse dimensionality reduction methods, such as Principal Component Analysis (PCA), Truncated Singular Value Decomposition (SVD), statistical feature selection (ANOVA F-value, Mutual Information), Recursive Feature Elimination (RFE), regularization-based LASSO selection, Random Forest feature importance, and Permutation Importance techniques. Empirical evaluation using the ASVSpoof 2017 v2.0 dataset measures the classification performance with the Equal Error Rate (EER) metric, achieving values of approximately 10%. Our comparative analysis demonstrates significant performance gains when dimensionality reduction methods are applied, underscoring their value in enhancing the security and effectiveness of voice biometric verification systems against emerging spoofing threats. (AU) | |
| Processo FAPESP: | 19/21464-1 - Redes de aprendizado profundo e seleção de características para detecção de fraudes em sistemas de autenticação biométrica por voz |
| Beneficiário: | Rodrigo Colnago Contreras |
| Modalidade de apoio: | Bolsas no Brasil - Pós-Doutorado |
| Processo FAPESP: | 21/12407-4 - Aprimorando os sistemas de autenticação biométrica por voz: robustez mediante disfonias de curta duração |
| Beneficiário: | Rodrigo Capobianco Guido |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 22/05186-4 - Aprimorando os Sistemas de Autenticação Biométrica por Voz: Robustez Mediante Disfonias de Curta Duração |
| Beneficiário: | Rodrigo Colnago Contreras |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |
| Processo FAPESP: | 23/06611-3 - Inteligência Comercial com Mineração de Dados |
| Beneficiário: | Monique Simplicio Viana |
| Modalidade de apoio: | Bolsas no Brasil - Programa Capacitação - Treinamento Técnico |