LATIDIA · Ciberseguridad

Una bolsa de trucos para defender los canales de reconocimiento facial basados en el aprendizaje profundo contra los ataques de puerta trasera

arXiv: 2611.06412v1Tipo de anuncio: Cross Resumen: La protección de los sistemas de reconocimiento facial maduros y de múltiples etapas es crucial para los proveedores de sistemas. Este informe técnico se dirige al modelo de amenaza más relevante para la pr de la industria

La noticia

arXiv:2611.06412v1 Announce Type: cross Abstract: The protection of mature, multi-stage Face Recognition Systems is crucial to system providers. This technical report targets the threat model most relevant to industry practitioners: although model training is performed in-house, training data is sourced from possibly-compromised third parties. Given this setting, we present three practical defense "tricks" against data poisoning-based backdoor attacks. The first operates at training-time, detecting poisoned identities during training or prior to a pipeline's deployment. The last two operate at inference-time, filtering suspicious inputs before an answer can be sent back to a potential attacker. Each method is presented with their motivation, algorithms and computational impact, and evaluated on realistic face