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METHODS AND APPARATUS TO DEFEND AGAINST ADVERSARIAL MACHINE LEARNING

机译:防御对抗机器学习的方法和设备

摘要

Methods, apparatus, systems and articles of manufacture to defend against adversarial machine learning are disclosed. An example apparatus includes a model trainer to train a classification model based on files with expected classifications; and a model modifier to select a convolution layer of the trained classification model based on an analysis of the convolution layers of the trained classification model; and replace the convolution layer with a tree-based structure to generate a modified classification model.
机译:公开了用于防止抗逆性机器学习的制造方法,装置,系统和制品。示例设备包括模型训练器,用于根据具有预期分类的文件训练分类模型;和模型修饰符,基于训练分类模型的卷积层的分析选择训练分类模型的卷积层;并用基于树的结构替换卷积层以生成修改后的分类模型。

著录项

  • 公开/公告号WO2021061264A1

    专利类型

  • 公开/公告日2021-04-01

    原文格式PDF

  • 申请/专利权人 MCAFEE LLC;

    申请/专利号WO2020US43306

  • 发明设计人 MATHEWS SHERIN M.;FRALICK CELESTE R.;

    申请日2020-07-23

  • 分类号G06N3/04;G06N3/08;G06N5;G06N5/04;G06N20/20;

  • 国家 US

  • 入库时间 2022-08-24 18:03:23

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