Deep Neural Rejection against Adversarial Examples
EURASIP Journal on Information Security, pp. 1-10, 2019.
Despite the impressive performances reported by deep neural networks in different application domains, they remain largely vulnerable to adversarial examples, i.e., input samples that are carefully perturbed to cause misclassification at test time. In this work, we propose a deep neural rejection mechanism to detect adversarial examples, ...More
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