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- W3048203754 abstract "We improve the robustness of Deep Neural Net (DNN) to adversarial attacks by using an interpolating function as the output activation. This data-dependent activation remarkably improves both the generalization and robustness of DNN. In the CIFAR10 benchmark, we raise the robust accuracy of the adversarially trained ResNet20 from begin{document}$ sim 46% $end{document} to begin{document}$ sim 69% $end{document} under the state-of-the-art Iterative Fast Gradient Sign Method (IFGSM) based adversarial attack. When we combine this data-dependent activation with total variation minimization on adversarial images and training data augmentation, we achieve an improvement in robust accuracy by 38.9 begin{document}$ % $end{document} for ResNet56 under the strongest IFGSM attack. Furthermore, We provide an intuitive explanation of our defense by analyzing the geometry of the feature space." @default.
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- W3048203754 date "2021-01-01" @default.
- W3048203754 modified "2023-09-23" @default.
- W3048203754 title "Adversarial defense via the data-dependent activation, total variation minimization, and adversarial training" @default.
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- W3048203754 doi "https://doi.org/10.3934/ipi.2020046" @default.
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