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README
Apache-2.0

MindArmour

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简介

MindArmour关注AI的安全和隐私问题。致力于增强模型的安全可信、保护用户的数据隐私。主要包含3个模块:对抗样本鲁棒性模块、Fuzz Testing模块、隐私保护与评估模块。

对抗样本鲁棒性模块

对抗样本鲁棒性模块用于评估模型对于对抗样本的鲁棒性,并提供模型增强方法用于增强模型抗对抗样本攻击的能力,提升模型鲁棒性。对抗样本鲁棒性模块包含了4个子模块:对抗样本的生成、对抗样本的检测、模型防御、攻防评估。

对抗样本鲁棒性模块的架构图如下:

mindarmour_architecture

Fuzz Testing模块

Fuzz Testing模块是针对AI模型的安全测试,根据神经网络的特点,引入神经元覆盖率,作为Fuzz测试的指导,引导Fuzzer朝着神经元覆盖率增加的方向生成样本,让输入能够激活更多的神经元,神经元值的分布范围更广,以充分测试神经网络,探索不同类型的模型输出结果和错误行为。

Fuzz Testing模块的架构图如下:

fuzzer_architecture

隐私保护模块

隐私保护模块包含差分隐私训练与隐私泄露评估。

差分隐私训练模块

差分隐私训练包括动态或者非动态的差分隐私SGDMomentumAdam优化器,噪声机制支持高斯分布噪声、拉普拉斯分布噪声,差分隐私预算监测包含ZCDP、RDP。

差分隐私的架构图如下:

dp_architecture

隐私泄露评估模块

隐私泄露评估模块用于评估模型泄露用户隐私的风险。利用成员推理方法来推测样本是否属于用户训练数据集,从而评估深度学习模型的隐私数据安全。

隐私泄露评估模块框架图如下:

privacy_leakage

开始

确认系统环境信息

  • 硬件平台为Ascend、GPU或CPU。
  • 参考MindSpore安装指南,完成MindSpore的安装。
    MindArmour与MindSpore的版本需保持一致。
  • 其余依赖请参见setup.py

安装

源码安装

  1. 从Gitee下载源码。

    git clone https://gitee.com/mindspore/mindarmour.git
  2. 在源码根目录下,执行如下命令编译并安装MindArmour。

    cd mindarmour
    python setup.py install

pip安装

pip install https://ms-release.obs.cn-north-4.myhuaweicloud.com/{version}/MindArmour/{arch}/mindarmour-{version}-cp37-cp37m-linux_{arch}.whl --trusted-host ms-release.obs.cn-north-4.myhuaweicloud.com -i https://mirrors.huaweicloud.com/repository/pypi/simple
  • 在联网状态下,安装whl包时会自动下载MindArmour安装包的依赖项(依赖项详情参见setup.py),其余情况需自行安装。
  • {version}表示MindArmour版本号,例如下载1.0.1版本MindArmour时,{version}应写为1.0.1。
  • {arch}表示系统架构,例如使用的Linux系统是x86架构64位时,{arch}应写为x86_64。如果系统是ARM架构64位,则写为aarch64

验证是否成功安装

执行如下命令,如果没有报错No module named 'mindarmour',则说明安装成功。

python -c 'import mindarmour'

文档

安装指导、使用教程、API,请参考用户文档

社区

社区问答:MindSpore Slack

贡献

欢迎参与社区贡献,详情参考Contributor Wiki

版本

版本信息参考:RELEASE

版权

Apache License 2.0

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A tool box for MindSpore users to enhance model security and trustworthiness. 展开 收起
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