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Defending against adversarial attacks by randomized diversification
[article]
2019
arXiv
pre-print
The vulnerability of machine learning systems to adversarial attacks questions their usage in many applications. In this paper, we propose a randomized diversification as a defense strategy. We introduce a multi-channel architecture in a gray-box scenario, which assumes that the architecture of the classifier and the training data set are known to the attacker. The attacker does not only have access to a secret key and to the internal states of the system at the test time. The defender
arXiv:1904.00689v1
fatcat:e6bxnxxf4je5regybarwelae6u