IAE: Irony-based Adversarial Examples for Sentiment Analysis Systems
This addresses security and reliability issues in NLP systems, particularly sentiment analysis, by highlighting vulnerabilities to irony-based attacks, though it is incremental as it applies a known adversarial concept to a new textual domain.
The paper tackles the problem of adversarial examples in text by proposing Irony-based Adversarial Examples (IAE), which transforms sentences into ironic ones to attack sentiment analysis systems, resulting in significant performance deterioration of state-of-the-art models.
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.