# Can audio-visual integration strengthen robustness under multimodal attacks?

arXiv: https://arxiv.org/abs/2104.02000 · HTML: https://mlsanity.com/paper/2104.02000/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 62 of its 95 references that are in the map.
- First posted on arXiv 5 Apr 2021.

## Builds on (most cited first, 30 of 62)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Improving Transferability of Adversarial Examples with Input Diversity](https://mlsanity.com/paper/1803.06978/) (2018)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [Look, Listen and Learn](https://mlsanity.com/paper/1705.08168/) (2017)
- [Mitigating Adversarial Effects Through Randomization](https://mlsanity.com/paper/1711.01991/) (2017)
- [What Makes Training Multi-Modal Classification Networks Hard?](https://mlsanity.com/paper/1905.12681/) (2019)
- [Audio-Visual Scene Analysis with Self-Supervised Multisensory Features](https://mlsanity.com/paper/1804.03641/) (2018)
- [Audio Adversarial Examples: Targeted Attacks on Speech-to-Text](https://mlsanity.com/paper/1801.01944/) (2018)
- [The Sound of Pixels](https://mlsanity.com/paper/1804.03160/) (2018)
- [Objects that Sound](https://mlsanity.com/paper/1712.06651/) (2017)
- [Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss](https://mlsanity.com/paper/1905.03820/) (2019)
- [Cooperative Learning of Audio and Video Models from Self-Supervised Synchronization](https://mlsanity.com/paper/1807.00230/) (2018)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [Lip Reading Sentences in the Wild](https://mlsanity.com/paper/1611.05358/) (2016)
- [EPIC-Fusion: Audio-Visual Temporal Binding for Egocentric Action Recognition](https://mlsanity.com/paper/1908.08498/) (2019)
- [Talking Face Generation by Adversarially Disentangled Audio-Visual Representation](https://mlsanity.com/paper/1807.07860/) (2018)
- [Learning to Localize Sound Source in Visual Scenes](https://mlsanity.com/paper/1803.03849/) (2018)
- [Self-Supervised Learning of Audio-Visual Objects from Video](https://mlsanity.com/paper/2008.04237/) (2020)
- [Listen to Look: Action Recognition by Previewing Audio](https://mlsanity.com/paper/1912.04487/) (2019)
- [Lip Movements Generation at a Glance](https://mlsanity.com/paper/1803.10404/) (2018)

## Built on by (most cited first, 23 of 23)

- [Sound Adversarial Audio-Visual Navigation](https://mlsanity.com/paper/2202.10910/) (2022)
- [Audio-Visual Class-Incremental Learning](https://mlsanity.com/paper/2308.11073/) (2023)
- [Benchmarking Robustness of Adaptation Methods on Pre-trained Vision-Language Models](https://mlsanity.com/paper/2306.02080/) (2023)
- [Robustness Analysis of Video-Language Models Against Visual and Language Perturbations](https://mlsanity.com/paper/2207.02159/) (2022)
- [Visual Sound Localization in the Wild by Cross-Modal Interference Erasing](https://mlsanity.com/paper/2202.06406/) (2022)
- [Pay Self-Attention to Audio-Visual Navigation](https://mlsanity.com/paper/2210.01353/) (2022)
- [Quantifying and Enhancing Multi-modal Robustness with Modality Preference](https://mlsanity.com/paper/2402.06244/) (2024)
- [A Survey on Safe Multi-Modal Learning System](https://mlsanity.com/paper/2402.05355/) (2024)
- [Bag of Tricks to Boost Adversarial Transferability](https://mlsanity.com/paper/2401.08734/) (2024)
- [Space-Time Memory Network for Sounding Object Localization in Videos](https://mlsanity.com/paper/2111.05526/) (2021)
- [Noise-Tolerant Learning for Audio-Visual Action Recognition](https://mlsanity.com/paper/2205.07611/) (2022)
- [Universal Adversarial Attack on Aligned Multimodal LLMs](https://mlsanity.com/paper/2502.07987/) (2025)
- [Understanding and Measuring Robustness of Multimodal Learning](https://mlsanity.com/paper/2112.12792/) (2021)
- [What Makes for Robust Multi-Modal Models in the Face of Missing Modalities?](https://mlsanity.com/paper/2310.06383/) (2023)
- [MMCert: Provable Defense against Adversarial Attacks to Multi-modal Models](https://mlsanity.com/paper/2403.19080/) (2024)
- [Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space](https://mlsanity.com/paper/2304.00436/) (2023)
- [Rethinking Audio-Visual Adversarial Vulnerability from Temporal and Modality Perspectives](https://mlsanity.com/paper/2502.11858/) (2025)
- [textttAVROBUSTBENCH: Benchmarking the Robustness of Audio-Visual Recognition Models at Test-Time](https://mlsanity.com/paper/2506.00358/) (2025)
- [Looking and Listening: Audio Guided Text Recognition](https://mlsanity.com/paper/2306.03482/) (2023)
- [Magmaw: Modality-Agnostic Adversarial Attacks on Machine Learning-Based Wireless Communication Systems](https://mlsanity.com/paper/2311.00207/) (2023)
- [SoundBreak: A Systematic Study of Audio-Only Adversarial Attacks on Trimodal Models](https://mlsanity.com/paper/2601.16231/) (2026)
- [Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter](https://mlsanity.com/paper/2407.19981/) (2024)
- [TAIL: Text-Audio Incremental Learning](https://mlsanity.com/paper/2503.04258/) (2025)

## Abstract (arXiv)

In this paper, we propose to make a systematic study on machines multisensory perception under attacks. We use the audio-visual event recognition task against multimodal adversarial attacks as a proxy to investigate the robustness of audio-visual learning. We attack audio, visual, and both modalities to explore whether audio-visual integration still strengthens perception and how different fusion mechanisms affect the robustness of audio-visual models. For interpreting the multimodal interactions under attacks, we learn a weakly-supervised sound source visual localization model to localize sounding regions in videos. To mitigate multimodal attacks, we propose an audio-visual defense approach based on an audio-visual dissimilarity constraint and external feature memory banks. Extensive experiments demonstrate that audio-visual models are susceptible to multimodal adversarial attacks; audio-visual integration could decrease the model robustness rather than strengthen under multimodal attacks; even a weakly-supervised sound source visual localization model can be successfully fooled; our defense method can improve the invulnerability of audio-visual networks without significantly sacrificing clean model performance.