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We present a novel method for generating robust adversarial image examplesbuilding upon the recent `deep image prior' (DIP) that exploits convolutionalnetwork architectures to enforce plausible texture in image synthesis.Adversarial images are commonly generated by perturbing images to introducehigh frequency noise that induces image misclassification, but that is fragileto subsequent digital manipulation of the image. We show that using DIP toreconstruct an image under adversarial constraint induces perturbations thatare more robust to affine deformation, whilst remaining visually imperceptible.Furthermore we show that our DIP approach can also be adapted to produce localadversarial patches (`adversarial stickers'). We demonstrate robust adversarialexamples over a broad gamut of images and object classes drawn from theImageNet dataset.

Robust Synthesis of Adversarial Visual Examples Using a Deep Image Prior