Data Publication

Trojan Detection Software Challenge - image-classification-aug2020-holdout

Michael Majurski Author's orcid, Timothy Blattner Author's orcid, Derek Juba Author's orcid, Neil Fendley, Kiran Karra Author's orcid, Chace Ashcraft Author's orcid
Contact: Michael Paul Majurski.
Identifier: doi:10.18434/mds2-2322
Version: 1.1...
Round 2 Holdout Dataset

The data being generated and disseminated is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of human level AIs trained to perform a variety of tasks (image classification, natural language processing, etc.). 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 144 trained, human level, image classification AI models using a variety of architectures. The models were trained on synthetically created image data of non-real traffic signs superimposed on road background scenes. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.
Research Areas
NIST R&D: Information Technology: Software researchInformation Technology: CybersecurityInformation Technology: Computational science
Keywords: Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;
These data are public.
Data and related material can be found at the following locations:
Version: 1.1...
Cite this dataset
Michael Majurski, Timothy Blattner, Derek Juba, Neil Fendley, Kiran Karra, Chace Ashcraft (2020), Trojan Detection Software Challenge - image-classification-aug2020-holdout, National Institute of Standards and Technology, https://doi.org/10.18434/mds2-2322 (Accessed 2025-03-21)
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NERDm
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