Integrating adversarial machine learning with Question Answering (QA) systems has emerged as a critical area for understanding the vulnerabilities and robustness of these systems. This article aims to comprehensively review adversarial example-generation techniques in the QA field, including textual and multimodal contexts. We examine the techniques employed through systematic categorization, providing a comprehensive, structured review. Beginning with an overview of traditional QA models, we traverse the adversarial example generation by exploring rule-based perturbations and advanced generative models. We then extend our research to include multimodal QA systems, analyze them across various methods, and examine generative models, seq2seq architectures, and hybrid methodologies. Our research grows to different defense strategies, adversarial datasets, and evaluation metrics and illustrates the comprehensive literature on adversarial QA. Finally, the paper considers the future landscape of adversarial question generation, highlighting potential research directions that can advance textual and multimodal QA systems in the context of adversarial challenges.

翻译：将对抗机器学习与问答（QA）系统相结合，已成为理解这些系统脆弱性与鲁棒性的关键研究领域。本文旨在全面评述问答领域的对抗样本生成技术，涵盖文本与多模态两个维度。通过系统分类，我们对所采用的技术进行了深入剖析，提供了结构化、综合性的综述。首先概述传统问答模型，进而探索基于规则的扰动与先进生成模型在对抗样本生成中的应用。随后将研究拓展至多模态问答系统，从多种方法（包括生成模型、序列到序列架构及混合方法）展开分析。研究进一步扩展至不同防御策略、对抗性数据集及评估指标，展现了对抗性问答领域的完整文献图景。最后，本文展望了对抗性问句生成的未来发展方向，指出了在对抗性挑战背景下推动文本与多模态问答系统进步的可能研究路径。