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Source note2 min read

Vulnerability of Natural Language Classifiers to Evolutionary Generated Adversarial Text

Source

arXiv

arxiv.org/abs/2606.27215v1

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Source

Plain-Language Brief

The paper presents GAversary, a new method for generating adversarial attacks on natural language models using a Genetic Algorithm (GA). This method is able to reduce the accuracy of several target models significantly compared to other attack methods like BAE and A2T. However, it perturbs slightly more words and takes around 5% more time.

What the Document Says

  • The paper introduces GAversary, a Genetic Algorithm for generating adversarial attacks on natural language models.
  • GAversary is able to treat the target model as a black box, using only the logit value output by the model.
  • GAversary uses GloVe embeddings to propose word replacements as its mutation operator.
  • GAversary is applied to several benchmark data sets and well-known target models.
  • GAversary reduces the accuracy of test data compared to BAE and A2T in some cases, with the best case reducing a 76.8% accuracy to 5.8%.
  • GAversary perturbs just under twice as many words as the other two methods.
  • GAversary has slightly lower semantic similarity to the original text compared to the other methods.
  • GAversary takes around a 5% increase in run-time.

Key Concepts Mentioned

  • Adversarial attacks
  • Deep learning models
  • Natural Language Processing (NLP)
  • Genetic Algorithm (GA)
  • GloVe embeddings

Claims Made by the Document

  • GAversary can substantially reduce the accuracy of target models on test data compared to BAE and A2T.
  • GAversary perturbs more words than the other two methods.

Evidence Used

  • Results from applying GAversary to benchmark data sets and well-known target models

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Deep learning models have achieved impressive performance across various fields but remain vulnerable to adversarial inputs, particularly in NLP, where such attacks can have significant real-world consequences. Adversarial attacks often involve small, semantically similar token replacements to fool NLP models, and recent methods have become more precise by targeting specific vulnerable words, often by exploiting some level of access to the model's internal structure. This paper proposes GAversary, a hybrid Genetic Algorithm (GA) to generate adversarial attacks on natural language models. The GA is able to treat the target model as a black box, requiring only the logit value output by the model to guide the search. GAversary differs from GAs previously proposed for this problem by using GloVe embeddings to propose word replacements (the mutation operator) to improve the semantic similarity of the adversarial examples. GAversary is applied to several benchmark data sets and well-known target models. GAversary is able to substantially reduce the target model's accuracy on test data compared to the BAE and A2T attacks compared against (in the best case, reducing a 76.8% accuracy to 5.8%, compared to BAE's 27.6%). The trade-off is that GAversary perturbs just under twice as many words as the other two methods, with a slightly lower semantic similarity to the original text and around a 5% increase in run-time.