From Text to Multimodal: A Comprehensive Survey of Adversarial Example
  Generation in Question Answering Systems
Gulsum Yigit Mehmet Fatih Amasyali
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.