A universal adversarial policy for text classifiers

Gallil Maimon, Lior Rokach

Research output: Contribution to journalArticlepeer-review

4 Scopus citations
77 Downloads (Pure)

Abstract

Discovering the existence of universal adversarial perturbations had large theoretical and practical impacts on the field of adversarial learning. In the text domain, most universal studies focused on adversarial prefixes which are added to all texts. However, unlike the vision domain, adding the same perturbation to different inputs results in noticeably unnatural inputs. Therefore, we introduce a new universal adversarial setup – a universal adversarial policy, which has many advantages of other universal attacks but also results in valid texts – thus making it relevant in practice. We achieve this by learning a single search policy over a predefined set of semantics preserving text alterations, on many texts. This formulation is universal in that the policy is successful in finding adversarial examples on new texts efficiently. Our approach uses text perturbations which were extensively shown to produce natural attacks in the non-universal setup (specific synonym replacements). We suggest a strong baseline approach for this formulation which uses reinforcement learning. Its ability to generalise (from as few as 500 training texts) shows that universal adversarial patterns exist in the text domain as well.

Original languageEnglish
Pages (from-to)282-291
Number of pages10
JournalNeural Networks
Volume153
DOIs
StatePublished - 1 Sep 2022

Keywords

  • Adversarial learning
  • NLP
  • Reinforcement learning
  • Text classification
  • Universal adversarial attacks

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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