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

arXiv: https://arxiv.org/abs/2409.02649 · HTML: https://mlsanity.com/paper/2409.02649/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment](https://mlsanity.com/paper/1907.11932/) (2019)
- [BERT-ATTACK: Adversarial Attack Against BERT Using BERT](https://mlsanity.com/paper/2004.09984/) (2020)
- [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (2018)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [BAE: BERT-based Adversarial Examples for Text Classification](https://mlsanity.com/paper/2004.01970/) (2020)
- [Adversarial Example Generation with Syntactically Controlled Paraphrase Networks](https://mlsanity.com/paper/1804.06059/) (2018)
- [Counter-fitting Word Vectors to Linguistic Constraints](https://mlsanity.com/paper/1603.00892/) (2016)
- [Contextualized Perturbation for Textual Adversarial Attack](https://mlsanity.com/paper/2009.07502/) (2020)
- [An LLM can Fool Itself: A Prompt-Based Adversarial Attack](https://mlsanity.com/paper/2310.13345/) (2023)
- [OpenAttack: An Open-source Textual Adversarial Attack Toolkit](https://mlsanity.com/paper/2009.09191/) (2020)
- [Verifying the Robustness of Automatic Credibility Assessment](https://mlsanity.com/paper/2303.08032/) (2023)
- [Fast Adversarial Training against Textual Adversarial Attacks](https://mlsanity.com/paper/2401.12461/) (2024)
- [A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models](https://mlsanity.com/paper/2402.11469/) (2024)

## Built on by (most cited first, 4 of 4)

- [Attacking Misinformation Detection Using Adversarial Examples Generated by Language Models](https://mlsanity.com/paper/2410.20940/) (2024)
- [Entity-aware Cross-lingual Claim Detection for Automated Fact-checking](https://mlsanity.com/paper/2503.15220/) (2025)
- [Robustness of Misinformation Classification Systems to Adversarial Examples Through BeamAttack](https://mlsanity.com/paper/2506.23661/) (2025)
- [OASIS: Optimizing Attacker Sequences for Hard-Label Black-Box Text Attacks](https://mlsanity.com/paper/2608.29568/) (2026)

## 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.