Abstract: Deep Neural Networks (DNNs) have recently made significant strides in various fields; however, they are susceptible to adversarial examples—crafted inputs with imperceptible perturbations ...
Abstract: Deep neural network-based classifiers are prone to errors when processing adversarial examples (AEs). AEs are minimally perturbed input data undetectable to humans posing significant risks ...
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