English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Integrating adversarial machine learning with Question Answering (QA) systemshas emerged as a critical area for understanding the vulnerabilities androbustness of these systems. This article aims to comprehensively reviewadversarial example-generation techniques in the QA field, including textualand multimodal contexts. We examine the techniques employed through systematiccategorization, providing a comprehensive, structured review. Beginning with anoverview of traditional QA models, we traverse the adversarial examplegeneration by exploring rule-based perturbations and advanced generativemodels. We then extend our research to include multimodal QA systems, analyzethem across various methods, and examine generative models, seq2seqarchitectures, and hybrid methodologies. Our research grows to differentdefense strategies, adversarial datasets, and evaluation metrics andillustrates the comprehensive literature on adversarial QA. Finally, the paperconsiders the future landscape of adversarial question generation, highlightingpotential research directions that can advance textual and multimodal QAsystems in the context of adversarial challenges.

From Text to Multimodal: A Comprehensive Survey of Adversarial Example Generation in Question Answering Systems