Data Publication

Trojan Detection Software Challenge - image-classification-sep2022-train

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/mds2-2831
Version: 1.0...
Round 11 Train Dataset

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of image classification AIs trained on synthetic image data build from Cityscapes. 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 288 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.
Research Areas
NIST R&D: Information Technology: CybersecurityInformation Technology: Software research
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: 1.0...
Cite this dataset
Michael Majurski, Timothy Blattner, Derek Juba, Neil Fendley, Kiran Karra, Chace Ashcraft (2022), Trojan Detection Software Challenge - image-classification-sep2022-train, National Institute of Standards and Technology, https://doi.org/10.18434/mds2-2831 (Accessed 2025-02-14)
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NERDm
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