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International Conference on Parallel Problem Solving from Nature

PPSN 2022: Parallel Problem Solving from Nature – PPSN XVII pp 341–355Cite as

Attention-Based Genetic Algorithm for Adversarial Attack in Natural Language Processing

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13398)

Abstract

Many recent studies have shown that deep neural networks (DNNs) are vulnerable to adversarial examples. Adversarial attacks on DNNs for natural language processing tasks are notoriously more challenging than that in computer vision. This paper proposes an attention-based genetic algorithm (dubbed AGA) for generating adversarial examples under a black-box setting. In particular, the attention mechanism helps identify the relatively more important words in a given text. Based on this information, bespoke crossover and mutation operators are developed to navigate AGA to focus on exploiting relatively more important words thus leading to a save of computational resources. Experiments on three widely used datasets demonstrate that AGA achieves a higher success rate with less than \(48\%\) of the number of queries than the peer algorithms. In addition, the underlying DNN can become more robust by using the adversarial examples obtained by AGA for adversarial training.

Keywords

  • Attention mechanism
  • Adversarial attack
  • Genetic algorithm
  • Natural language processing

This work was supported by UKRI Future Leaders Fellowship (MR/S017062/1), EPSRC (2404317), NSFC (62076056), Royal Society (IES/R2/212077) and Amazon Research Award.

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Notes

  1. 1.

    https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews.

  2. 2.

    https://www.kaggle.com/amananandrai/ag-news-classification-dataset.

  3. 3.

    https://nlp.stanford.edu/projects/snli/.

  4. 4.

    https://huggingface.co/.

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Authors and Affiliations

  1. College of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Shasha Zhou

  2. Department of Computer Science, University of Exeter, Exeter, EX4 5DS, UK

    Ke Li & Geyong Min

Authors
  1. Shasha Zhou
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  2. Ke Li
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  3. Geyong Min
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Corresponding author

Correspondence to Ke Li .

Editor information

Editors and Affiliations

  1. TU Dortmund, Dortmund, Germany

    Günter Rudolph

  2. Leiden University, Leiden, The Netherlands

    Anna V. Kononova

  3. Shinshu University, Nagano, Japan

    Hernán Aguirre

  4. Technische Universität Dresden, Dresden, Germany

    Pascal Kerschke

  5. University of Stirling, Stirling, UK

    Gabriela Ochoa

  6. Jožef Stefan Institute, Ljubljana, Slovenia

    Tea Tušar

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Zhou, S., Li, K., Min, G. (2022). Attention-Based Genetic Algorithm for Adversarial Attack in Natural Language Processing. In: Rudolph, G., Kononova, A.V., Aguirre, H., Kerschke, P., Ochoa, G., Tušar, T. (eds) Parallel Problem Solving from Nature – PPSN XVII. PPSN 2022. Lecture Notes in Computer Science, vol 13398. Springer, Cham. https://doi.org/10.1007/978-3-031-14714-2_24

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  • DOI: https://doi.org/10.1007/978-3-031-14714-2_24

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  • Online ISBN: 978-3-031-14714-2

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