LATIDIA · Ciberseguridad

Ataques de ejemplo contenciosos de Black-Box a sistemas electorales basados en aprendizaje automático

arXiv:2611.02166v1 Anuncio Tipo: nuevo Resumen: Los modelos de aprendizaje automático han demostrado una precisión excepcional para la clasificación de papeletas en sistemas electorales. Investigaciones recientes han demostrado que estos modelos de clasificación de papeletas

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arXiv:2611.02166v1 Announce Type: new Abstract: Machine learning models have shown exceptional accuracy for ballot classification in election systems. Recent research has demonstrated that these ballot classification models are vulnerable to adversarial example attacks. This is a critical security issue because a successful attack could alter the outcome of an election and undermine the democratic process. However, all adversarial example attacks in the voting domain have assumed a white-box threat model, where the classifier parameters are known. In this work, we advance the field of machine learning in election systems by analyzing a realistic black-box adversary. We implement and evaluate eleven state-of-the-art black-box attacks: query based attacks ADBA, Square, SurFree, RayS,