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Ensemble-Based Biometric Verification: Defending Against Multi-Strategy Deepfake Image Generation

H. Zen, R. Wagh, M. Wanderley, G. Bicalho, R. Park, M. Sun, R. Palacios, L. Carvalho, G. Rinaldo, A. Gupta

Computers Vol. 14, nº. 6, pp. 225

Resumen:

Deepfake images, synthetic images created using digital software, continue to present a serious threat to online platforms. This is especially relevant for biometric verification systems, as deepfakes that attempt to bypass such measures increase the risk of impersonation, identity theft and scams. Although research on deepfake image detection has provided many high-performing classifiers, many of these commonly used detection models lack generalizability across different methods of deepfake generation. For companies and governments fighting identify fraud, a lack of generalization is challenging, as malicious actors may use a variety of deepfake image-generation methods available through online wrappers. This work explores if combining multiple classifiers into an ensemble model can improve generalization without losing performance across different generation methods. It also considers current methods of deepfake image generation, with a focus on publicly available and easily accessible methods. We compare our framework against its underlying models to show how companies can better respond to emerging deepfake generation methods.


Resumen divulgativo:

La detección de imágenes deepfake es un componente muy relevante para reducir la suplantación, robo de identidad y engaños. Este trabajo combina varios clasificadores en un ensemble para mejorar la capacidad de generalización ante diferentes métodos de generación de deepfake.


Palabras Clave: deepfakes; biometric verification systems; generalization; ensemble learning; deepfake detection model


Índice de impacto JCR-JIF y cuartil WoS: 5,200 - Q2 (2025)

Referencia DOI: DOI icon https://doi.org/10.3390/computers14060225

Publicado en papel: Junio 2025.

Publicado on-line: Junio 2025.



Cita:
H. Zen, R. Wagh, M. Wanderley, G. Bicalho, R. Park, M. Sun, R. Palacios, L. Carvalho, G. Rinaldo, A. Gupta, "Ensemble-Based Biometric Verification: Defending Against Multi-Strategy Deepfake Image Generation", Computers, Vol. 14, nº. 6, pp. 225, Junio 2025. [Online: Junio 2025] doi: 10.3390/computers14060225

    Líneas de investigación:
  • IA segura, confiable, justa e interpretable
  • Deep reality analysis
  • Machine Learning y Analítica Avanzada
  • Incorporación de la inteligencia artificial y big data en las estrategias empresariales
  • Tecnologías de la Información y Comunicación (TIC)
    Grupos de investigación:
  • Instituto de Investigación Tecnológica (IIT)
    ODS:
  • Objetivo 17: Alianzas para lograr objetivos
  • Objetivo 8: Trabajo decente y crecimiento económico