Breaking Transferability of Adversarial Samples with Randomness

11 May 2018Yan ZhouMurat KantarciogluBowei Xi

We investigate the role of transferability of adversarial attacks in the observed vulnerabilities of Deep Neural Networks (DNNs). We demonstrate that introducing randomness to the DNN models is sufficient to defeat adversarial attacks, given that the adversary does not have an unlimited attack budget... (read more)

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