Audio-Visual Event Recognition through the lens of Adversary

TL;DR

This paper investigates the robustness of audio-visual classification models against adversarial noises, analyzing fusion strategies, feature contributions, and neural module vulnerabilities to improve understanding and robustness.

Contribution

It introduces a comprehensive study of adversarial attacks on multimodal audio-visual models, revealing insights into fusion strategies and feature robustness.

Findings

Early/middle/late fusion impacts robustness and accuracy

Different frequency/time features contribute variably to robustness

Neural modules exhibit distinct vulnerabilities to adversarial noise

Abstract

As audio/visual classification models are widely deployed for sensitive tasks like content filtering at scale, it is critical to understand their robustness along with improving the accuracy. This work aims to study several key questions related to multimodal learning through the lens of adversarial noises: 1) The trade-off between early/middle/late fusion affecting its robustness and accuracy 2) How do different frequency/time domain features contribute to the robustness? 3) How do different neural modules contribute to the adversarial noise? In our experiment, we construct adversarial examples to attack state-of-the-art neural models trained on Google AudioSet. We compare how much attack potency in terms of adversarial perturbation of size $\epsilon$ using different $L_p$ norms we would need to "deactivate" the victim model. Using adversarial noise to ablate multimodal models, we are able to provide insights into what is the best potential fusion strategy to balance the model parameters/accuracy and robustness trade-off and distinguish the robust features versus the non-robust features that various neural networks model tend to learn.