{"id":13640697,"url":"https://github.com/spring-epfl/trickster","last_synced_at":"2025-04-20T06:34:31.974Z","repository":{"id":37777655,"uuid":"132155180","full_name":"spring-epfl/trickster","owner":"spring-epfl","description":"Library and experiments for attacking machine learning in discrete domains","archived":true,"fork":false,"pushed_at":"2022-12-08T01:27:53.000Z","size":14857,"stargazers_count":45,"open_issues_count":23,"forks_count":4,"subscribers_count":6,"default_branch":"master","last_synced_at":"2024-08-03T01:17:34.998Z","etag":null,"topics":["adversarial-machine-learning","graph-algorithms","machine-learning"],"latest_commit_sha":null,"homepage":"https://arxiv.org/abs/1810.10939","language":"Jupyter 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Notebook","readme":".. image:: https://raw.githubusercontent.com/spring-epfl/trickster/master/trickster.svg?sanitize=true\n   :width: 100px\n   :alt: Trickster\n\n=========\ntrickster\n=========\n\n|travis| |docs|\n\n.. |docs| image:: https://readthedocs.org/projects/trickster-lib/badge/?version=latest\n   :target: https://trickster-lib.readthedocs.io/en/latest/\n   :alt: Docs\n\n.. |travis| image:: https://travis-ci.org/spring-epfl/trickster.svg?branch=master\n   :target: https://travis-ci.org/spring-epfl/trickster\n   :alt: Travis\n\n.. description-marker-do-not-remove\n\nLibrary and experiments for attacking machine learning in discrete domains `using graph search\n\u003chttps://arxiv.org/abs/1810.10939\u003e`__.\n\n.. end-description-marker-do-not-remove\n\nSee the `documentation \u003chttps://trickster-lib.readthedocs.io/en/latest/\u003e`__ on Readthedocs, or jump\ndirectly to the `guide \u003chttps://trickster-lib.readthedocs.io/en/latest/guide.html\u003e`__.\n\nSetup\n=====\n\nLibrary\n-------\n\n.. lib-setup-marker-do-not-remove\n\nInstall the trickster library as a Python package:\n\n::\n\n    pip install -e git+git://github.com/spring-epfl/trickster#egg=trickster\n\nNote that trickster requires Python **3.6**.\n\n.. end-lib-setup-marker-do-not-remove\n\nExperiments\n-----------\n\n.. exp-setup-marker-do-not-remove\n\nPython packages\n~~~~~~~~~~~~~~~\n\nInstall the required Python packages:\n\n::\n\n    pip install -r requirements.txt\n\nSystem packages\n~~~~~~~~~~~~~~~\n\nOn Ubuntu, you need these system packages:\n\n::\n\n    apt install parallel unzip\n\nDatasets\n~~~~~~~~\n\nTo download the datasets, run this:\n\n::\n\n    make data\n\nThe datasets include:\n\n- UCI `German credit dataset \u003chttps://archive.ics.uci.edu/ml/datasets/statlog+(german+credit+data)\u003e`__\n- Zafar Gilani's `Twitter bot classification dataset \u003chttps://www.cl.cam.ac.uk/~szuhg2/data.html\u003e`__\n- Tao Wang's `knndata \u003chttp://home.cse.ust.hk/~taow/wf/data/\u003e`__\n\n.. end-exp-setup-marker-do-not-remove\n\nCiting\n======\n\n.. citing-marker-do-not-remove\n\nThis is an accompanying code to the paper \"`Evading classifiers in discrete domains with provable\noptimality guarantees \u003chttps://arxiv.org/abs/1810.10939\u003e`__\" by B. Kulynych, J. Hayes, N. Samarin,\nand C. Troncoso, 2018. Cite as follows:\n\n.. code-block:: bibtex\n\n    @article{KulynychHST18,\n      author    = {Bogdan Kulynych and\n                   Jamie Hayes and\n                   Nikita Samarin and\n                   Carmela Troncoso},\n      title     = {Evading classifiers in discrete domains with provable optimality guarantees},\n      journal   = {CoRR},\n      volume    = {abs/1810.10939},\n      year      = {2018},\n      url       = {http://arxiv.org/abs/1810.10939},\n      archivePrefix = {arXiv},\n      eprint    = {1810.10939},\n    }\n\n.. end-citing-marker-do-not-remove\n\nAcknowledgements\n================\n\n.. acks-marker-do-not-remove\n\nThis 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.\n\n.. end-acks-marker-do-not-remove\n","funding_links":[],"categories":["Adversarial Robustness Libraries","Adversarial Robustness","Jupyter Notebook"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspring-epfl%2Ftrickster","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fspring-epfl%2Ftrickster","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fspring-epfl%2Ftrickster/lists"}