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A library for Secure and Explainable Machine Learning

Project description

SecML: A library for Secure and Explainable Machine Learning

SecML is an open-source Python library for the security evaluation of Machine Learning (ML) algorithms.

It comes with a set of powerful features:

  • Wide range of supported ML algorithms. All supervised learning algorithms supported by scikit-learn are available, as well as Neural Networks (NNs) through PyTorch deep learning platform.
  • Built-in attack algorithms. Evasion and poisoning attacks based on a custom-developed fast solver. In addition, we provide connectors to other third-party Adversarial Machine Learning libraries.
  • Dense/Sparse data support. We provide full, transparent support for both dense (through numpy library) and sparse data (through scipy library) in a single data structure.
  • Visualize your results. We provide visualization and plotting framework, based on the widely-known library matplotlib.
  • Explain your results. Explainable ML methods to interpret model decisions via influential features and prototypes.
  • Model Zoo. Use our pre-trained models to save time and easily replicate scientific results.
  • Multi-processing. Do you want to save time further? We provide full compatibility with all the multi-processing features of scikit-learn and pytorch, along with built-in support of the joblib library.
  • Extensible. Easily create new components, like ML models or attack algorithms, by extending the provided abstract interfaces.

SecML is currently in development.

If you encounter any bug, please report them using the GitLab issue tracker.
Please see our ROADMAP for an overview of the future development directions.

Status Alpha Python 3.5 | 3.6 | 3.7 Platform Linux | MacOS Apache License 2.0

Installation Guide

We recommend instaling SecML in a specific environment along with its dependencies.

Common frameworks to create and manage envs are virtualenv and conda. Both alternatives provide convenient user guides on how to properly setup the envs, so this guide will not cover the configuration procedure.

Operating System requirements

SecML can run under Python >= 3.5 with no additional configuration steps required, as all its dependencies are available as wheel packages for the primary macOS versions and Linux distributions.

However, to support additional advanced features more packages can be necessary depending on the Operating System used:

  • Linux (Ubuntu >= 16.04 or equivalent dist)

  • macOS (macOS >= 10.12 Sierra)

    • Nothing to note.

Installation process

Before starting the installation process try to obtain the latest version of the pip manager by calling: pip install -U pip

The setup process is managed by the Python package setuptools. Be sure to obtain the latest version by calling: pip install -U setuptools

Once the environment is set up, SecML can installed and run by multiple means:

  1. Install from official PyPI repository:

    • pip install secml
  2. Install from wheel/zip package (https://pypi.python.org/pypi/secml#files):

    • pip install <package-file>

In all cases, the setup process will try to install the correct dependencies. In case something goes wrong during the install process, try to install the dependencies first by calling: pip install -r requirements.txt

SecML should now be importable in python via: import secml.

To update a current installation using any of the previous methods, add the -U parameter after the pip install directive. Please see our Update Guides for specific upgrade intructions depending on the source and target version.

Extra Components

SecML comes with a set of extras components that can be installed if desired.

To specify the extra components to install, add the section [extras] while calling pip install. extras will be a comma-separated list of components you want to install. Example:

  • pip install secml[extra1,extra2]

All the installation procedures via pip described above allow definition of the [extras] section.

Available extra components

  • pytorch : Neural Networks (NNs) through PyTorch deep learning platform.
    Will install: torch >= 1.1, torchvision >= 0.2.2
  • cleverhans : Wrapper of CleverHans, a Python library to benchmark vulnerability of machine learning systems to adversarial examples. Will install: tensorflow >= 1.14.*, < 2, cleverhans
  • tf-gpu : Shortcut for installing TensorFlow package with GPU support.
    Will install: tensorflow-gpu >= 1.14.*, < 2

Usage Guide

SecML is based on numpy, scipy, scikit-learn and pytorch, widely-used packages for scientific computing and machine learning with Python.

As a result, most of the interfaces of the library should be pretty familiar to frequent users of those packages.

The primary data class is the secml.array.CArray, multi-dimensional (currently limited to 2 dimensions) array structure which embeds both dense and sparse data accepting as input numpy.ndarray and scipy.sparse.csr_matrix (more sparse formats will be supported soon). This structure is the standard input and output of all other classes in the library.

The secml.ml package contains all the Machine Learning algorithms and support classes, including classifiers, loss and regularizer functions, kernels and performance evaluation functions. Also, a zoo of pre-trained models is provided by the secml.ml.model_zoo package.

The secml.adv package contains evasion and poisoning attacks based on a custom-developed solver, along with classes to easily perform security evaluation of Machine Learning algorithms.

The secml.explanation package contains different explainable Machine Learning methods that allow interpreting classifiers decisions by analyzing the relevant components such as features or training prototypes.

The secml.figure package contains a visualization and plotting framework based on matplotlib.

Developers and Contributors

The contributing and developer's guide is available at: https://secml.gitlab.io/developers/

Authors

This library is maintained by PRALab - Pattern Recognition and Applications Lab.

List of contributors:

  • Marco Melis (maintainer) [1]
  • Ambra Demontis [1]
  • Maura Pintor [1], [2]
  • Battista Biggio [1], [2]

[1] Department of Electrical and Electronic Engineering, University of Cagliari, Italy
[2] Pluribus One, Italy

Credits

Acknowledgements

SecML has been partially developed with the support of European Union’s ALOHA project Horizon 2020 Research and Innovation programme, grant agreement No. 780788.

Copyright

SecML has been developed by PRALab - Pattern Recognition and Applications lab and Pluribus One s.r.l. under Apache License 2.0. Copyright 2019.

Project details


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