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Trojan Detection Software Challenge - nlp-sentiment-classification-mar2021-train
Round 5 Train DatasetThe data being generated and disseminated is the train data used to construct 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 1656 adversarially trained, sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from movie and product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present. Errata: The following models were contaminated during dataset packaging. This caused nominally clean models to have a trigger. Please avoid using these models. Due to the similarity between the Round5 and Round6 datasets (both contain similarly trained sentiment classification AI models), the dataset authors suggest ignoring the Round5 data and only using the Round6 dataset. Corrupted Models: [id-00000007, id-00000014, id-00000030, id-00000036, id-00000047, id-00000074, id-00000080, id-00000088, id-00000089, id-00000097, id-00000103, id-00000105, id-00000122, id-00000123, id-00000124, id-00000127, id-00000148, id-00000151, id-00000154, id-00000162, id-00000165, id-00000181, id-00000184, id-00000185, id-00000193, id-00000197, id-00000198, id-00000207, id-00000230, id-00000236, id-00000239, id-00000240, id-00000244, id-00000251, id-00000256, id-00000258, id-00000265, id-00000272, id-00000284, id-00000321, id-00000336, id-00000364, id-00000389, id-00000391, id-00000396, id-00000423, id-00000425, id-00000446, id-00000449, id-00000463, id-00000468, id-00000479, id-00000499, id-00000516, id-00000524, id-00000532, id-00000537, id-00000563, id-00000575, id-00000577, id-00000583, id-00000592, id-00000629, id-00000635, id-00000643, id-00000644, id-00000685, id-00000710, id-00000720, id-00000724, id-00000730, id-00000735, id-00000780, id-00000784, id-00000794, id-00000798, id-00000802, id-00000808, id-00000818, id-00000828, id-00000841, id-00000864, id-00000867, id-00000923, id-00000970, id-00000971, id-00000973, id-00000989, id-00000990, id-00000996, id-00001000, id-00001036, id-00001040, id-00001041, id-00001044, id-00001048, id-00001053, id-00001059, id-00001063, id-00001116, id-00001131, id-00001139, id-00001146, id-00001159, id-00001163, id-00001166, id-00001171, id-00001183, id-00001188, id-00001201, id-00001211, id-00001233, id-00001251, id-00001262, id-00001291, id-00001300, id-00001302, id-00001305, id-00001312, id-00001314, id-00001327, id-00001341, id-00001344, id-00001346, id-00001364, id-00001365, id-00001373, id-00001389, id-00001390, id-00001391, id-00001392, id-00001399, id-00001414, id-00001418, id-00001425, id-00001449, id-00001470, id-00001486, id-00001516, id-00001517, id-00001518, id-00001532, id-00001533, id-00001537, id-00001542, id-00001549, id-00001579, id-00001580, id-00001581, id-00001586, id-00001591, id-00001599, id-00001600, id-00001604, id-00001610, id-00001618, id-00001643, id-00001650]
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Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-train
공공데이터포털
Round 6 Train DatasetThis 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 48 adversarially trained, sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from movie and product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present.
Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-train part2
공공데이터포털
Round 6 Train Dataset part2This 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.
Trojan Detection Software Challenge - nlp-sentiment-classification-mar2021-test
공공데이터포털
Round 5 Test DatasetThis is the test 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 504 adversarially trained, sentiment classification AI models using a small set of model architectures. The models were trained on text data drawn from movie and product reviews. Half (50%) of the models have been poisoned with an embedded trigger which causes misclassification of the images when the trigger is present. Errata: The following models were contaminated during dataset packaging. This caused nominally clean models to have a trigger. Please avoid using these models. Due to the similarity between the Round5 and Round6 datasets (both contain similarly trained sentiment classification AI models), the dataset authors suggest ignoring the Round5 data and only using the Round6 dataset. Corrupted Models: [id-00000000, id-00000003, id-00000004, id-00000005, id-00000011, id-00000022, id-00000074, id-00000076, id-00000084, id-00000091, id-00000094, id-00000147, id-00000149, id-00000156, id-00000159, id-00000162, id-00000166, id-00000168, id-00000171, id-00000176, id-00000178, id-00000216, id-00000217, id-00000220, id-00000222, id-00000223, id-00000227, id-00000233, id-00000238, id-00000239, id-00000246, id-00000290, id-00000293, id-00000301, id-00000314, id-00000323, id-00000367, id-00000368, id-00000369, id-00000372, id-00000379, id-00000388, id-00000433, id-00000438, id-00000441, id-00000447, id-00000451]
Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-test
공공데이터포털
Round 6 Test DatasetThis is the test 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 480 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.
Trojan Detection Software Challenge - nlp-named-entity-recognition-may2021-train
공공데이터포털
Round 7 Train DatasetThis 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 named entity recognition (NER) 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 192 sentiment classification 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.
Trojan Detection Software Challenge - nlp-question-answering-aug2023-train
공공데이터포털
nlp-question-answering-aug2023-trainThis is the train data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform 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.
Trojan Detection Software Challenge - nlp-question-answering-sep2021-train
공공데이터포털
Round 8 Train DatasetThis 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 extractive question answering (QA 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 120 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.
Trojan Detection Software Challenge - image-classification-jun2020-train
공공데이터포털
Round 1 Training DatasetThe data being generated and disseminated is the training data used to construct 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 1000 trained, human level, image classification AI models using the following architectures (Inception-v3, DenseNet-121, and ResNet50). 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. Errata: This dataset had a software bug in the trigger embedding code that caused 4 models trained for this dataset to have a ground truth value of 'poisoned' but which did not contain any triggers embedded. These models should not be used. Models Without a Trigger Embedded: id-00000184 id-00000599 id-00000858 id-00001088 Google Drive Mirror: https://drive.google.com/open?id=1uwVt3UCRL2fCX9Xvi2tLoz_z-DwbU6Ce
Trojan Detection Software Challenge - nlp-named-entity-recognition-may2021-test
공공데이터포털
Round 7 Test DatasetThis is the test 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 named entity recognition (NER) 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 384 named entity recognition 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.
Trojan Detection Software Challenge - nlp-sentiment-classification-apr2021-holdout
공공데이터포털
Round 6 Holdout DatasetThis is the holdout 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 480 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.
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