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Apr 27, 2020 · This work presents a new adversarial attack based on the modeling and exploitation of class-wise and layer-wise deep feature distributions. We ...
Almost all current adversarial attacks of CNN classifiers rely on information de- rived from the output layer of the network.
This work presents a new adversarial attack based on the modeling and exploitation of class-wise and layer-wise deep feature distributions that achieves ...
Nov 30, 2020 · Transferable Perturbations of Deep Feature Distributions. Accepted Papers by Program Chairs • Transferable Perturbations of Deep Feature ...
This is an official release of the paper Learning Transferable Adversarial Perturbations. images. Abstract. While effective, deep neural networks (DNNs) are ...
Feb 11, 2020 · This work presents a new adversarial attack based on the modeling and exploitation of class-wise and layer-wise deep feature distributions. We ...
Jul 4, 2022 · Bibliographic details on Transferable Perturbations of Deep Feature Distributions.
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What are adversarial perturbations?
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FIM first focuses on generating perturbed features by imitating diverse patterns from multi-domain sources. Instead of exploiting the original inputs' diversity ...
This repo lists relevant papers summarized in our survey paper. A Survey on Transferability of Adversarial Examples across Deep Neural Networks. Jindong Gu, ...
To this end, we propose Transferable Adversarial Training (TAT) to enable the adaptation of deep classifiers. The approach generates ...