  In various real-world applications such as machine translation, sentiment
analysis, and question answering, a pivotal role is played by NLP models,
facilitating efficient communication and decision-making processes in domains
ranging from healthcare to finance. However, a significant challenge is posed
to the robustness of these natural language processing models by text
adversarial attacks. These attacks involve the deliberate manipulation of input
text to mislead the predictions of the model while maintaining human
interpretability. Despite the remarkable performance achieved by
state-of-the-art models like BERT in various natural language processing tasks,
they are found to remain vulnerable to adversarial perturbations in the input
text. In addressing the vulnerability of text classifiers to adversarial
attacks, three distinct attack mechanisms are explored in this paper using the
victim model BERT: BERT-on-BERT attack, PWWS attack, and Fraud Bargain's Attack
(FBA). Leveraging the IMDB, AG News, and SST2 datasets, a thorough comparative
analysis is conducted to assess the effectiveness of these attacks on the BERT
classifier model. It is revealed by the analysis that PWWS emerges as the most
potent adversary, consistently outperforming other methods across multiple
evaluation scenarios, thereby emphasizing its efficacy in generating
adversarial examples for text classification. Through comprehensive
experimentation, the performance of these attacks is assessed and the findings
indicate that the PWWS attack outperforms others, demonstrating lower runtime,
higher accuracy, and favorable semantic similarity scores. The key insight of
this paper lies in the assessment of the relative performances of three
prevalent state-of-the-art attack mechanisms.
