Detecting Adversarial Examples through Nonlinear Dimensionality Reduction

Deep neural networks are vulnerable to adversarial examples, i.e., carefully-perturbed inputs aimed to mislead classification. This work proposes a detection method based on combining non-linear dimensionality reduction and density estimation techniques... (read more)

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