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armory-library

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 27 Oct 2023Publisher:Zenodo
Authors: Wartell, Matt; Treubig, Kyle; Suggs, Sterling;

doi: 10.5281/zenodo.10041829 , 10.5281/zenodo.10045693 , 10.5281/zenodo.10269627 , 10.5281/zenodo.10213938 , 10.5281/zenodo.10655747

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.

If you use this software, please cite it using the metadata from this file.

Keywords

adversarial machine learning

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average