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- W3162819547 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 does different frequency/time domain features contribute to the robustness? 3) How does different neural modules contribute against the adversarial noise? In our experiment, we construct adversarial examples to attack state-of-the-art neural models trained on Google AudioSet.[1] <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> We compare how much attack potency in terms of adversarial perturbation of size using different L <inf xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>p</inf> 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." @default.
- W3162819547 created "2021-05-24" @default.
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- W3162819547 date "2021-06-06" @default.
- W3162819547 modified "2023-09-23" @default.
- W3162819547 title "Audio-Visual Event Recognition Through the Lens of Adversary" @default.
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- W3162819547 doi "https://doi.org/10.1109/icassp39728.2021.9415065" @default.
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