# From text to multimodal: a survey of adversarial example generation in question answering systems

arXiv: https://arxiv.org/abs/2312.16156 · HTML: https://mlsanity.com/paper/2312.16156/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 47 of its 100 references that are in the map.
- First posted on arXiv 26 Dec 2023.

## Builds on (most cited first, 30 of 47)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [HellaSwag: Can a Machine Really Finish Your Sentence?](https://mlsanity.com/paper/1905.07830/) (2019)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs](https://mlsanity.com/paper/1903.00161/) (2019)
- [Are NLP Models really able to Solve Simple Math Word Problems?](https://mlsanity.com/paper/2103.07191/) (2021)
- [ELI5: Long Form Question Answering](https://mlsanity.com/paper/1907.09190/) (2019)
- [How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering](https://mlsanity.com/paper/2012.00955/) (2020)
- [AmbigQA: Answering Ambiguous Open-domain Questions](https://mlsanity.com/paper/2004.10645/) (2020)
- [SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference](https://mlsanity.com/paper/1808.05326/) (2018)
- [Large-Scale Adversarial Training for Vision-and-Language Representation Learning](https://mlsanity.com/paper/2006.06195/) (2020)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [Pathologies of Neural Models Make Interpretations Difficult](https://mlsanity.com/paper/1804.07781/) (2018)
- [Measure and Improve Robustness in NLP Models: A Survey](https://mlsanity.com/paper/2112.08313/) (2021)
- [Overcoming Language Priors in Visual Question Answering with Adversarial Regularization](https://mlsanity.com/paper/1810.03649/) (2018)
- [DolphinAtack: Inaudible Voice Commands](https://mlsanity.com/paper/1708.09537/) (2017)
- [Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT](https://mlsanity.com/paper/2003.04985/) (2020)
- [Human-Adversarial Visual Question Answering](https://mlsanity.com/paper/2106.02280/) (2021)
- [Generating Natural Questions About an Image](https://mlsanity.com/paper/1603.06059/) (2016)
- [Answer-based Adversarial Training for Generating Clarification Questions](https://mlsanity.com/paper/1904.02281/) (2019)
- [VX2TEXT: End-to-End Learning of Video-Based Text Generation From Multimodal Inputs](https://mlsanity.com/paper/2101.12059/) (2021)
- [Visual Question Generation as Dual Task of Visual Question Answering](https://mlsanity.com/paper/1709.07192/) (2017)
- [Information Maximizing Visual Question Generation](https://mlsanity.com/paper/1903.11207/) (2019)
- [Adversarial Examples in Modern Machine Learning: A Review](https://mlsanity.com/paper/1911.05268/) (2019)
- [DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection](https://mlsanity.com/paper/2305.13625/) (2023)
- [Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models](https://mlsanity.com/paper/2105.11136/) (2021)
- [Semantic Equivalent Adversarial Data Augmentation for Visual Question Answering](https://mlsanity.com/paper/2007.09592/) (2020)
- [Adversarial Regularization for Visual Question Answering: Strengths, Shortcomings, and Side Effects](https://mlsanity.com/paper/1906.08430/) (2019)
- [Creativity: Generating Diverse Questions using Variational Autoencoders](https://mlsanity.com/paper/1704.03493/) (2017)
- [High-Order Attention Models for Visual Question Answering](https://mlsanity.com/paper/1711.04323/) (2017)

## Abstract (arXiv)

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.