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 (throughscipy
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
andpytorch
, 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.
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)
python3-tk
, for running MatplotLib Tk-based backends;- NVIDIA® CUDA® Toolkit for running
tf-gpu
extra component. See the TensorFlow Guide.
-
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:
-
Install from official PyPI repository:
pip install secml
-
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 installingTensorFlow
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.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/
How to cite SecML
If you use SecML in a scientific publication, please cite the following paper:
secml: A Python Library for Secure and Explainable Machine Learning, Melis et al., arXiv preprint arXiv:1912.10013 (2019).
BibTeX entry:
@article{melis2019secml,
title={secml: A Python Library for Secure and Explainable Machine Learning},
author={Melis, Marco and Demontis, Ambra and Pintor, Maura and Sotgiu, Angelo and Biggio, Battista},
journal={arXiv preprint arXiv:1912.10013},
year={2019}
}
Authors
This library is maintained by PRALab - Pattern Recognition and Applications Lab.
List of contributors:
- Marco Melis [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
numpy
Travis E, Oliphant. "A guide to NumPy", USA: Trelgol Publishing, 2006.scipy
Travis E. Oliphant. "Python for Scientific Computing", Computing in Science & Engineering, 9, 10-20, 2007.scikit-learn
Pedregosa et al., "Scikit-learn: Machine Learning in Python", JMLR 12, pp. 2825-2830, 2011.matplotlib
J. D. Hunter, "Matplotlib: A 2D Graphics Environment", Computing in Science & Engineering, vol. 9, no. 3, pp. 90-95, 2007.pytorch
Paszke, Adam, et al. "Automatic differentiation in pytorch.", NIPS-W, 2017.cleverhans
Papernot, Nicolas, et al. "Technical Report on the CleverHans v2.1.0 Adversarial Examples Library." arXiv preprint arXiv:1610.00768 (2018).
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. All rights reserved.
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