Adversarial examples, which are inputs deliberately perturbed with imperceptible changes to induce model errors, have raised serious concerns for the reliability and security of deep neural networks (DNNs). While adversarial attacks have been extensively studied in continuous data domains such as images, the discrete nature of text presents unique challenges. In this paper, we propose Irony-based Adversarial Examples (IAE), a method that transforms straightforward sentences into ironic ones to create adversarial text. This approach exploits the rhetorical device of irony, where the intended meaning is opposite to the literal interpretation, requiring a deeper understanding of context to detect. The IAE method is particularly challenging due to the need to accurately locate evaluation words, substitute them with appropriate collocations, and expand the text with suitable ironic elements while maintaining semantic coherence. Our research makes the following key contributions: (1) We introduce IAE, a strategy for generating textual adversarial examples using irony. This method does not rely on pre-existing irony corpora, making it a versatile tool for creating adversarial text in various NLP tasks. (2) We demonstrate that the performance of several state-of-the-art deep learning models on sentiment analysis tasks significantly deteriorates when subjected to IAE attacks. This finding underscores the susceptibility of current NLP systems to adversarial manipulation through irony. (3) We compare the impact of IAE on human judgment versus NLP systems, revealing that humans are less susceptible to the effects of irony in text.

本研究针对情感分析系统中对抗样本的安全性问题，提出了一种基于讽刺的对抗样本生成方法（IAE）。该方法通过将简单句子转化为讽刺句子，在保留语义一致性的同时，挑战了现有情感分析模型的鲁棒性，显示出这些模型对讽刺语言的脆弱性，并表明人类在理解讽刺时具备更好的判断能力。

IAE：基于讽刺的对抗样本用于情感分析系统