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README.md

MindArmour

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What is MindArmour

A tool box for MindSpore users to enhance model security and trustworthiness and protect privacy data.

MindArmour model security module is designed for adversarial examples, including four submodule: adversarial examples generation, adversarial examples detection, model defense and evaluation. The architecture is shown as follow：

mindarmour_architecture

MindArmour differential privacy module Differential-Privacy implements the differential privacy optimizer. Currently, SGD, Momentum and Adam are supported. They are differential privacy optimizers based on the Gaussian mechanism.
This mechanism supports both non-adaptive and adaptive policy. Rényi differential privacy (RDP) and Zero-Concentrated differential privacy（ZDP） are provided to monitor differential privacy budgets. The architecture is shown as follow：

dp_architecture

Setting up MindArmour

Dependencies

This library uses MindSpore to accelerate graph computations performed by many machine learning models. Therefore, installing MindSpore is a pre-requisite. All other dependencies are included in setup.py.

Installation

Installation for development

  1. Download source code from Gitee.
git clone https://gitee.com/mindspore/mindarmour.git
  1. Compile and install in MindArmour directory.
$ cd mindarmour
$ python setup.py install

Pip installation

  1. Download whl package from MindSpore website, then run the following command:
pip install mindarmour-{version}-cp37-cp37m-linux_{arch}.whl
  1. Successfully installed, if there is no error message such as No module named 'mindarmour' when execute the following command:
python -c 'import mindarmour'

Docs

Guidance on installation, tutorials, API, see our User Documentation.

Community

Contributing

Welcome contributions. See our Contributor Wiki for more details.

Release Notes

The release notes, see our RELEASE.

License

Apache License 2.0

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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。

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