Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions

14 Apr 2020Jon VadilloRoberto SantanaJose A. Lozano

Despite the remarkable performance and generalization levels of deep learning models in a wide range of artificial intelligence tasks, it has been demonstrated that these models can be easily fooled by the addition of imperceptible but malicious perturbations to natural inputs. These altered inputs are known in the literature as adversarial examples... (read more)

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