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Mejora de la Transferibilidad Adversarial con Aplanamiento del Paisaje de Costos

Zhipeng Wei, Jingjing Chen, Feng Han

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    Este estudio presenta un nuevo método para mejorar la transferibilidad de ejemplos adversariales aplanando el paisaje de pérdidas de entrada. Al optimizar las perturbaciones tanto en máximos como en mínimos locales, el ataque CLEF mejora el éxito del ataque entre modelos.

    Palabras clave:
    aprendizaje automáticoseguridadvisión por computadorainteligencia artificialataques adversarialestransferibilidad adversarialpaisaje de pérdidasmáximos localesmínimos localesataque CLEF

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    Área de la Ciencia:

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

    Sus antecedentes:

    • Los ejemplos adversariales demuestran vulnerabilidades en los modelos de aprendizaje automático.
    • La transferibilidad de los ejemplos adversariales, especialmente los dirigidos, es crucial para los ataques prácticos.
    • Los métodos existentes se centran en optimizar las perturbaciones en los máximos del paisaje de pérdidas, ignorando los mínimos.

    Objetivo del estudio:

    • Demostrar teórica y empíricamente que aplanar el paisaje de pérdidas de entrada mejora la transferibilidad adversarial.
    • Proponer un método de ataque novedoso y eficiente en costos que considera tanto los máximos como los mínimos locales para mejorar la transferibilidad.

    Principales métodos:

    • Se analizó teóricamente el impacto del aplanamiento del paisaje de pérdidas en la transferibilidad adversarial.
    • Se propuso el ataque CLEF (Cost-efficient LandscapE Flattening).
    • Se utilizó la reutilización de gradientes para optimizar hacia máximos locales.
    • Se empleó modelado probabilístico para optimizar hacia mínimos locales, permitiendo el pre-entrenamiento.

    Principales resultados:

    • Se demostró que la optimización de perturbaciones tanto en máximos como en mínimos locales aplana el paisaje de pérdidas.
    • El ataque CLEF mejora significativamente la transferibilidad adversarial dirigida en comparación con los métodos existentes.
    • El modelado probabilístico para la optimización de mínimos se puede pre-entrenar, lo que reduce el costo del ataque.

    Conclusiones:

    • Aplanar el paisaje de pérdidas considerando tanto los máximos como los mínimos es una estrategia eficaz para mejorar la transferibilidad adversarial.
    • El ataque CLEF propuesto ofrece un método eficiente en costos y potente para generar ejemplos adversariales transferibles.
    • Esta investigación abre nuevas vías para comprender y mejorar la robustez adversarial y las metodologías de ataque.