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Trojan Detection Software Challenge - Round 8 Test Dataset
type: Dataset
contact point: Organization
  fn: Michael Paul Majurski
keyword: geo
keyword: geoss
keyword: united-states
keyword: amerigeoss
keyword: artificial-intelligence
keyword: adversarial-machine-learning
keyword: trojan-detection
keyword: national
keyword: ai
keyword: amerigeo
keyword: ckan
keyword: machine-learning
keyword: north-america
theme: Software research"
theme: Cybersecurity"}
description: This is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 360 QA AI models using a small set of model architectures. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.
issued: 2022-01-26
language: {en}
modified: 2021-08-09T00:00:00

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DOI Access for Trojan Detection Software Challenge - Round 8 Test Dataset
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