mlsanityData to 7 Oct 2026

OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation

arXiv 2409.02649 ↗First posted 4 Sep 2024cs.CL cs.AIcited by 4

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Abstract (arXiv)

This paper presents the experiments and results for the CheckThat! Lab at CLEF 2024 Task 6: Robustness of Credibility Assessment with Adversarial Examples (InCrediblAE). The primary objective of this task was to generate adversarial examples in five problem domains in order to evaluate the robustness of widely used text classification methods (fine-tuned BERT, BiLSTM, and RoBERTa) when applied to credibility assessment issues. This study explores the application of ensemble learning to enhance adversarial attacks on natural language processing (NLP) models. We systematically tested and refined several adversarial attack methods, including BERT-Attack, Genetic algorithms, TextFooler, and CLARE, on five datasets across various misinformation tasks. By developing modified versions of BERT-Attack and hybrid methods, we achieved significant improvements in attack effectiveness. Our results demonstrate the potential of modification and combining multiple methods to create more sophisticated and effective adversarial attack strategies, contributing to the development of more robust and secure systems.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 14 of its 25 references that are in the map. First posted on arXiv 4 Sep 2024.

Builds on

14 of 25 references.

References · 14

  1. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,196
  2. 2019Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailmentcited by 663
  3. 2020BERT-ATTACK: Adversarial Attack Against BERT Using BERTcited by 488
  4. 2018Generating Natural Language Adversarial Examplescited by 422
  5. 2018Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifierscited by 341
  6. 2020BAE: BERT-based Adversarial Examples for Text Classificationcited by 316
  7. 2018Adversarial Example Generation with Syntactically Controlled Paraphrase Networkscited by 282
  8. 2016Counter-fitting Word Vectors to Linguistic Constraintscited by 138
  9. 2020Contextualized Perturbation for Textual Adversarial Attackcited by 136
  10. 2023An LLM can Fool Itself: A Prompt-Based Adversarial Attackcited by 111
  11. 2020OpenAttack: An Open-source Textual Adversarial Attack Toolkitcited by 84
  12. 2023Verifying the Robustness of Automatic Credibility Assessmentcited by 8
2 more
  1. 2024Fast Adversarial Training against Textual Adversarial Attackscited by 7
  2. 2024A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Modelscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2024Attacking Misinformation Detection Using Adversarial Examples Generated by Language Modelscited by 10
  2. 2025Entity-aware Cross-lingual Claim Detection for Automated Fact-checkingcited by 3
  3. 2025Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttackcited by 0
  4. 2026OASIS: Optimizing Attacker Sequences for Hard-Label Black-Box Text Attackscited by 0