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Ataque adversario basado en decisiones y eficiente en consultas con bajo presupuesto de consultas

Yangzhuo Tuo1, Mengge Yin1, Shengbing Che2

  • 1School of Electronic Information and Physics, Central South University of Forestry and Technology, Changsha, 410004, China.

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Este estudio presenta un nuevo ataque basado en decisiones llamado One Plane One Query Attack (OPOQA) para crear ejemplos adversarios para redes neuronales profundas de manera eficiente. OPOQA mejora significativamente las tasas de éxito de los ataques con menos consultas, mejorando la investigación sobre la seguridad del modelo.

Palabras clave:
ataques adversariosredes neuronales profundasseguridad de aprendizaje automáticovisión por computadoraaprendizaje automático

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Área de la Ciencia:

  • Inteligencia Artificial
  • Visión por Computadora
  • Seguridad del Aprendizaje Automático

Sus antecedentes:

  • Las redes neuronales profundas (DNN) logran un alto rendimiento pero son vulnerables a ejemplos adversarios.
  • Los ataques a modelos del mundo real son desafiantes debido al acceso limitado a los internos del modelo, lo que hace que los ataques basados en decisiones sean cruciales.
  • Los ataques existentes basados en decisiones requieren numerosas consultas, lo que limita su aplicación práctica.

Objetivo del estudio:

  • Desarrollar un método de ataque novedoso y eficiente basado en decisiones para generar ejemplos adversarios de alta calidad.
  • Reducir el presupuesto de consultas necesario para ataques adversarios exitosos en DNN.
  • Mejorar el análisis de seguridad de las DNN contra manipulaciones adversarias.

Principales métodos:

  • Se introdujo el método One Plane One Query Attack (OPOQA).
  • Se empleó una estrategia de generación de ejemplos candidatos dentro de un plano 2D muestreado del espacio de la transformada discreta de coseno (DCT) de baja frecuencia.
  • Se utilizó la transformada discreta de coseno inversa (iDCT) para transformar las muestras de nuevo al espacio de píxeles, consultando solo un candidato por iteración para minimizar el número de consultas.

Principales resultados:

  • Se lograron altas tasas de éxito de ataque en modelos convencionales como Inception-v3, ResNet y VGG en el conjunto de datos ImageNet.
  • Se demostró un rendimiento superior en comparación con el algoritmo EPCA de última generación, con tasas de éxito de ataque que aumentaron un 4,5% y un 11,5% en los umbrales de RMSE de 0,05 y 0,01, respectivamente.
  • Se generaron con éxito ejemplos adversarios virtualmente imperceptibles dentro de un bajo presupuesto de consultas (1000 consultas).

Conclusiones:

  • OPOQA es un ataque basado en decisiones eficaz y eficiente para generar ejemplos adversarios de alta calidad.
  • El método propuesto reduce significativamente la complejidad de las consultas para ataques adversarios, haciéndolos más prácticos.
  • Esta investigación contribuye a la comprensión y mejora de la robustez de seguridad de las redes neuronales profundas.