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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 install
Pip
installationpip install mindarmour-{version}-cp37-cp37m-linux_{arch}.whl
No 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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