TextAttack: A Framework for Adversarial Attacks in Natural Language Processing

29 Apr 2020John X. MorrisEli LiflandJin Yong YooYanjun Qi

TextAttack is a library for running adversarial attacks against natural language processing (NLP) models. TextAttack builds attacks from four components: a search method, goal function, transformation, and a set of constraints... (read more)

PDF Abstract

Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet