MindArmour
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 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：
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
- Download source code from Gitee.
git clone https://gitee.com/mindspore/mindarmour.git- Compile and install in MindArmour directory.
$ cd mindarmour
$ python setup.py install
Pip installation
- Download whl package from MindSpore website, then run the following command:
pip install mindarmour-{version}-cp37-cp37m-linux_{arch}.whl- 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
- MindSpore Slack - Ask questions and find answers.
Contributing
Welcome contributions. See our Contributor Wiki for more details.
Release Notes
The release notes, see our RELEASE.