CITATION.cff

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cff-version: 1.2.0
title: armory-library
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - given-names: Matt
    family-names: Wartell
    email: armory@twosixtech.com
  - given-names: Kyle
    family-names: Treubig
    email: armory@twosixtech.com
  - given-names: Sterling
    family-names: Suggs
    email: armory@twosixtech.com
repository-code: 'https://github.com/twosixlabs/armory-library'
url: 'https://www.armory-library.org/'
abstract: >-
  Armory-library is a library for evaluating adversarial
  attack machine learning models. It is a pure Python
  installable which is intended to be used in a user created
  application. The armory-library API is intentionally small
  and simple to afford "today" speed of integration.
  Armory-library uses PyTorch, Lightning.AI, and IBM's
  Adversarial Robustness Toolbox (ART) to effect its
  evaluations; it logs all pararmeters and metrics to MLFlow
  either locally or to a remote server.


  Armory-library is part of the Armory project.
keywords:
  - adversarial machine learning
license: MIT
commit: 1ab0924b6eeca7720b26d66289686b37e81c9830
version: 23.10.4
date-released: '2023-10-25'
doi: 10.5281/zenodo.10041830