U.S. flag

An official website of the United States government

Dot gov

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Https

Secure .gov websites use HTTPS
A lock () or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Breadcrumb

  1. Home

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

Round 11 Train DatasetThis 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.

About this Dataset

Updated: 2024-02-22
Metadata Last Updated: 2022-10-24 00:00:00
Date Created: N/A
Data Provided by:
Dataset Owner: N/A

Access this data

Contact dataset owner Access URL
Landing Page URL
Table representation of structured data
Title Trojan Detection Software Challenge - image-classification-sep2022-train
Description Round 11 Train DatasetThis 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.
Modified 2022-10-24 00:00:00
Publisher Name National Institute of Standards and Technology
Contact mailto:[email protected]
Keywords Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;
{
    "identifier": "ark:\/88434\/mds2-2831",
    "accessLevel": "public",
    "contactPoint": {
        "hasEmail": "mailto:[email protected]",
        "fn": "Michael Paul Majurski"
    },
    "programCode": [
        "006:045"
    ],
    "landingPage": "https:\/\/data.nist.gov\/od\/id\/mds2-2831",
    "title": "Trojan Detection Software Challenge - image-classification-sep2022-train",
    "description": "Round 11 Train DatasetThis 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.",
    "language": [
        "en"
    ],
    "distribution": [
        {
            "accessURL": "https:\/\/drive.google.com\/drive\/folders\/1H38dLM4lXeGlPidms3TB2Y2Z38MOKcMy?usp=drive_link",
            "title": "image-classification-sep2022-train"
        }
    ],
    "bureauCode": [
        "006:55"
    ],
    "modified": "2022-10-24 00:00:00",
    "publisher": {
        "@type": "org:Organization",
        "name": "National Institute of Standards and Technology"
    },
    "theme": [
        "Information Technology:Cybersecurity",
        "Information Technology:Software research"
    ],
    "keyword": [
        "Trojan Detection; Artificial Intelligence; AI; Machine Learning; Adversarial Machine Learning;"
    ]
}