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While a substantial body of prior work has explored adversarial examplegeneration for natural language understanding tasks, these examples are oftenunrealistic and diverge from the real-world data distributions. In this work,we introduce a two-stage adversarial example generation framework(NaturalAdversaries), for designing adversaries that are effective at fooling agiven classifier and demonstrate natural-looking failure cases that couldplausibly occur during in-the-wild deployment of the models.  At the first stage a token attribution method is used to summarize a givenclassifier's behaviour as a function of the key tokens in the input. In thesecond stage a generative model is conditioned on the key tokens from the firststage. NaturalAdversaries is adaptable to both black-box and white-boxadversarial attacks based on the level of access to the model parameters. Ourresults indicate these adversaries generalize across domains, and offerinsights for future research on improving robustness of neural textclassification models.

NaturalAdversaries: Can Naturalistic Adversaries Be as Effective as Artificial Adversaries?