In this work, we take steps towards a formal characterization of adversarial perturbations by deriving lower bounds on the magnitudes of perturbations necessary ...
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In this work, we take steps towards a formal characterization of adversarial perturbations by deriving lower bounds on the magnitudes of perturbations necessary ...
This paper introduces lower bounds on the minimum adversarial perturbations that can be efficiently computed through layer-wise composition.
In this work, we take steps towards a formal characterization of adversarial perturbations by deriving lower bounds on the magnitudes of perturbations necessary ...
This work derives lower bounds on the magnitudes of perturbations necessary to change the classification of neural networks by experimentally verified on ...
Our framework provides lower bounds on adversarial robustness ... Distillation as a defense to adversarial perturbations against deep neural networks.
In this work, we take steps towards a formal characterization of adversarial perturbations by deriving lower bounds on the magnitudes of perturbations necessary ...
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Mar 4, 2024 · Lower Bounds on Adversarial Robustness from Optimal Transport. 33rd Conference on Neural Information Processing Systems, October 2019. [18]
Jan 28, 2022 · Summary Of The Paper: The authors present a method for deriving a lower bound on the loss of a specific model architecture under adversarial ...
Optimizing over only linear classifiers and translate-and-pair transportation plans or couplings is sufficient to lower bound the adversarial robustness.