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To deflect adversarial attacks, a range of "certified" classifiers have beenproposed. In addition to labeling an image, certified classifiers produce (whenpossible) a certificate guaranteeing that the input image is not an$\ell_p$-bounded adversarial example. We present a new attack that exploits notonly the labelling function of a classifier, but also the certificategenerator. The proposed method applies large perturbations that place imagesfar from a class boundary while maintaining the imperceptibility property ofadversarial examples. The proposed "Shadow Attack" causes certifiably robustnetworks to mislabel an image and simultaneously produce a "spoofed"certificate of robustness.

Breaking certified defenses: Semantic adversarial examples with spoofed robustness certificates