Rewriting Meaningful Sentences via Conditional BERT Sampling and an application on fooling text classifiers

Understand

Most adversarial attack methods that are designed to deceive a text classifier change the text classifier's prediction by modifying a few words or characters.

• Few try to attack classifiers by rewriting a whole sentence, due to the difficulties inherent in sentence-level rephrasing as well as the problem of setting the criteria for legitimate rewriting.
• In this paper, we explore the problem of creating adversarial examples with sentence-level rewriting.
• We design a new sampling method, named ParaphraseSampler, to efficiently rewrite the original sentence in multiple ways.