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SmoothFool: An Efficient Framework for Computing Smooth Adversarial Perturbations
[article]
2019
arXiv
pre-print
Deep neural networks are susceptible to adversarial manipulations in the input domain. The extent of vulnerability has been explored intensively in cases of ℓ_p-bounded and ℓ_p-minimal adversarial perturbations. However, the vulnerability of DNNs to adversarial perturbations with specific statistical properties or frequency-domain characteristics has not been sufficiently explored. In this paper, we study the smoothness of perturbations and propose SmoothFool, a general and computationally
arXiv:1910.03624v1
fatcat:tt6633nnanebla2ym4bpwa2jzy