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Exploiting epistemic uncertainty of the deep learning models to generate adversarial samples
(Springer, 2022-03)
Deep neural network (DNN) architectures are considered to be robust to random perturbations. Nevertheless, it was shown that they could be severely vulnerable to slight but carefully crafted perturbations of the input, ...
Closeness and uncertainty aware adversarial examples detection in adversarial machine learning
(Elsevier Ltd, 2022-07)
While deep learning models are thought to be resistant to random perturbations, it has been demonstrated that these architectures are vulnerable to deliberately crafted perturbations, albeit being quasi-imperceptible. These ...
Uncertainty as a Swiss army knife: new adversarial attack and defense ideas based on epistemic uncertainty
(Springer, 2022-04-02)
Although state-of-the-art deep neural network models are known to be robust to random perturbations, it was verified that these architectures are indeed quite vulnerable to deliberately crafted perturbations, albeit being ...