most citedExploiting epistemic uncertainty of the deep learning models to generate adversarial samples

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cs.LG2021★ 1 cited

Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples

Omer Faruk Tuna, Ferhat Ozgur Catak, M. Taner Eskil

Deep neural network architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully cra…

cs.LG2020

Closeness and Uncertainty Aware Adversarial Examples Detection in Adversarial Machine Learning

Omer Faruk Tuna, Ferhat Ozgur Catak, M. Taner Eskil

While state-of-the-art Deep Neural Network (DNN) models are considered to be robust to random perturbations, it was shown that these architectures are highly vulnerable to delibera…