Trojan Detection Software Challenge - nlp-sentiment-classification-mar2021-test

Round 5 Test Dataset
This 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]

第五轮测试数据集系用于构建与评估木马检测软件解决方案的测试数据。该数据由美国国家标准与技术研究院（NIST）生成，包含自然语言处理（NLP）人工智能，经过训练以对英文文本执行文本情感分类。其中已知比例的AI模型被注入了已知触发器，导致其产生错误行为。该数据集旨在开发用于检测通过嵌入式触发器被污染的训练AI模型的软件解决方案。该数据集由504个经过对抗性训练的情感分类AI模型组成，这些模型采用一组有限的模型架构。模型在电影和产品评论的文本数据上进行了训练。其中一半（50%）的模型被注入了嵌入式触发器，当触发器激活时，会导致图像分类错误。更正：在数据集打包过程中，以下模型遭受了污染，导致原本无污染的模型含有触发器。请避免使用这些模型。鉴于第五轮数据集与第六轮数据集（两者均包含类似训练的情感分类AI模型）的相似性，数据集作者建议忽略第五轮数据，仅使用第六轮数据集。受污染的模型：[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]