Trojan Detection Software Challenge - Round 6 Train Dataset part2

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 text sentiment classification 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 96 sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.

Data and Resources

Field Value
accessLevel public
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identifier ark:/88434/mds2-2405
issued 2021-05-14
landingPage https://data.nist.gov/od/id/mds2-2405
language {en}
license https://www.nist.gov/open/license
modified 2021-03-22 00:00:00
programCode {006:045}
publisher National Institute of Standards and Technology
resource-type Dataset
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theme {"Information Technology:Software research","Information Technology:Cybersecurity"}
Groups
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  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • ckan
  • geo
  • geoss
  • national
  • north-america
  • trojan-detection-artificial-intelligence-ai-machine-learning-adversarial-machine-learning
  • united-states
isopen False
license_id other-license-specified
license_title other-license-specified
maintainer Michael Paul Majurski
maintainer_email michael.majurski@nist.gov
metadata_created 2025-11-21T13:38:41.047960
metadata_modified 2025-11-21T13:38:41.047964
notes 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 text sentiment classification 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 96 sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the input when the trigger is present.
num_resources 3
num_tags 9
title Trojan Detection Software Challenge - Round 6 Train Dataset part2