https://github.com/spring-epfl/trickster

Library and experiments for attacking machine learning in discrete domains
https://github.com/spring-epfl/trickster

adversarial-machine-learning graph-algorithms machine-learning

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Library and experiments for attacking machine learning in discrete domains

• Host: GitHub
• URL: https://github.com/spring-epfl/trickster
• Owner: spring-epfl
• License: mit
• Archived: true
• Created: 2018-05-04T15:06:49.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2022-12-08T01:27:53.000Z (about 2 years ago)
• Last Synced: 2024-08-03T01:17:34.998Z (6 months ago)
• Topics: adversarial-machine-learning, graph-algorithms, machine-learning
• Language: Jupyter Notebook
• Homepage: https://arxiv.org/abs/1810.10939
• Size: 14.2 MB
• Stars: 45
• Watchers: 6
• Forks: 4
• Open Issues: 23
• Metadata Files:
  • Readme: README.rst
  • License: LICENSE.txt

Awesome Lists containing this project

• awesome-production-machine-learning - Trickster - epfl/trickster.svg?style=social) - Library and experiments for attacking machine learning in discrete domains using graph search. (Adversarial Robustness Libraries)
• awesome-production-machine-learning - Trickster - epfl/trickster.svg?style=social) - Library and experiments for attacking machine learning in discrete domains using graph search. (Adversarial Robustness)

README

        
.. image:: https://raw.githubusercontent.com/spring-epfl/trickster/master/trickster.svg?sanitize=true

   :width: 100px

   :alt: Trickster

=========

trickster

=========

|travis| |docs|

.. |docs| image:: https://readthedocs.org/projects/trickster-lib/badge/?version=latest

   :target: https://trickster-lib.readthedocs.io/en/latest/

   :alt: Docs

.. |travis| image:: https://travis-ci.org/spring-epfl/trickster.svg?branch=master

   :target: https://travis-ci.org/spring-epfl/trickster

   :alt: Travis

.. description-marker-do-not-remove

Library and experiments for attacking machine learning in discrete domains `using graph search

`__.

.. end-description-marker-do-not-remove

See the `documentation `__ on Readthedocs, or jump

directly to the `guide `__.

Setup

=====

Library

-------

.. lib-setup-marker-do-not-remove

Install the trickster library as a Python package:

::

    pip install -e git+git://github.com/spring-epfl/trickster#egg=trickster

Note that trickster requires Python **3.6**.

.. end-lib-setup-marker-do-not-remove

Experiments

-----------

.. exp-setup-marker-do-not-remove

Python packages

~~~~~~~~~~~~~~~

Install the required Python packages:

::

    pip install -r requirements.txt

System packages

~~~~~~~~~~~~~~~

On Ubuntu, you need these system packages:

::

    apt install parallel unzip

Datasets

~~~~~~~~

To download the datasets, run this:

::

    make data

The datasets include:

- UCI `German credit dataset `__

- Zafar Gilani's `Twitter bot classification dataset `__

- Tao Wang's `knndata `__

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Citing

======

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This is an accompanying code to the paper "`Evading classifiers in discrete domains with provable

optimality guarantees `__" by B. Kulynych, J. Hayes, N. Samarin,

and C. Troncoso, 2018. Cite as follows:

.. code-block:: bibtex

    @article{KulynychHST18,

      author    = {Bogdan Kulynych and

                   Jamie Hayes and

                   Nikita Samarin and

                   Carmela Troncoso},

      title     = {Evading classifiers in discrete domains with provable optimality guarantees},

      journal   = {CoRR},

      volume    = {abs/1810.10939},

      year      = {2018},

      url       = {http://arxiv.org/abs/1810.10939},

      archivePrefix = {arXiv},

      eprint    = {1810.10939},

    }

.. end-citing-marker-do-not-remove

Acknowledgements

================

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This work is funded by the NEXTLEAP project within the European Union’s Horizon 2020 Framework Programme for Research and Innovation (H2020-ICT-2015, ICT-10-2015) under grant agreement 688722.

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