Trojan Detection Software Challenge - Round 9 Train Dataset
type: Dataset
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 one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. 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 210 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: 2022-01-24T00:00:00
contact point: Organization
  fn: Michael Paul Majurski
keyword: adversarial-machine-learning
keyword: ai
keyword: amerigeo
keyword: amerigeoss
keyword: artificial-intelligence
keyword: ckan
keyword: geo
keyword: geoss
keyword: machine-learning
keyword: national
keyword: north-america
keyword: trojan-detection
keyword: united-states
theme: Cybersecurity"}
theme: Software research"

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DOI Access for Trojan Detection Software Challenge - Round 9 Train Dataset
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US Migrating
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