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Neural networks are vulnerable to adversarially-constructed perturbations oftheir inputs. Most research so far has considered perturbations of a fixedmagnitude under some $l_p$ norm. Although studying these attacks is valuable,there has been increasing interest in the construction of (and robustness to)unrestricted attacks, which are not constrained to a small and ratherartificial subset of all possible adversarial inputs. We introduce a novelalgorithm for generating such unrestricted adversarial inputs which, unlikeprior work, is adaptive: it is able to tune its attacks to the classifier beingtargeted. It also offers a 400-2,000x speedup over the existing state of theart. We demonstrate our approach by generating unrestricted adversarial inputsthat fool classifiers robust to perturbation-based attacks. We also show that,by virtue of being adaptive and unrestricted, our attack is able to defeatadversarial training against it.

Adaptive Generation of Unrestricted Adversarial Inputs