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Natural images are virtually surrounded by low-density misclassified regionsthat can be efficiently discovered by gradient-guided search --- enabling thegeneration of adversarial images. While many techniques for detecting theseattacks have been proposed, they are easily bypassed when the adversary hasfull knowledge of the detection mechanism and adapts the attack strategyaccordingly. In this paper, we adopt a novel perspective and regard theomnipresence of adversarial perturbations as a strength rather than a weakness.We postulate that if an image has been tampered with, these adversarialdirections either become harder to find with gradient methods or havesubstantially higher density than for natural images. We develop a practicaltest for this signature characteristic to successfully detect adversarialattacks, achieving unprecedented accuracy under the white-box setting where theadversary is given full knowledge of our detection mechanism.

A New Defense Against Adversarial Images: Turning a Weakness into a Strength