Abstract:Deep neural networks (DNNs) have become the technology of choice for realizing a variety of complex tasks. However, as highlighted by many recent studies, even an imperceptible perturbation to a correctly classified input can lead to misclassification by a DNN. This renders DNNs vulnerable to strategic input manipulations by attackers, and also prone to oversensitivity to environmental noise. To mitigate this phenomenon, practitioners apply joint classification by an ensemble of DNNs. By aggregating the classi… Show more
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