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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 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：
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.
git clone https://gitee.com/mindspore/mindarmour.git$ cd mindarmour
$ python setup.py installPip installationpip install mindarmour-{version}-cp37-cp37m-linux_{arch}.whlNo module named 'mindarmour' when execute the following command:python -c 'import mindarmour'Guidance on installation, tutorials, API, see our User Documentation.
Welcome contributions. See our Contributor Wiki for more details.
The release notes, see our RELEASE.
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