  We introduce the Text Classification Attack Benchmark (TCAB), a dataset for
analyzing, understanding, detecting, and labeling adversarial attacks against
text classifiers. TCAB includes 1.5 million attack instances, generated by
twelve adversarial attacks targeting three classifiers trained on six source
datasets for sentiment analysis and abuse detection in English. Unlike standard
text classification, text attacks must be understood in the context of the
target classifier that is being attacked, and thus features of the target
classifier are important as well. TCAB includes all attack instances that are
successful in flipping the predicted label; a subset of the attacks are also
labeled by human annotators to determine how frequently the primary semantics
are preserved. The process of generating attacks is automated, so that TCAB can
easily be extended to incorporate new text attacks and better classifiers as
they are developed. In addition to the primary tasks of detecting and labeling
attacks, TCAB can also be used for attack localization, attack target labeling,
and attack characterization. TCAB code and dataset are available at
https://react-nlp.github.io/tcab/.

Machine Learning