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.
@article{arxiv.2409.02649,
title = {OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation},
author = {Włodzimierz Lewoniewski and Piotr Stolarski and Milena Stróżyna and Elzbieta Lewańska and Aleksandra Wojewoda and Ewelina Księżniak and Marcin Sawiński},
journal= {arXiv preprint arXiv:2409.02649},
year = {2024}
}
Comments
CLEF 2024 - Conference and Labs of the Evaluation Forum