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Deep neural networks (DNNs) are known to be vulnerable to adversarialexamples. Existing works have mostly focused on either digital adversarialexamples created via small and imperceptible perturbations, or physical-worldadversarial examples created with large and less realistic distortions that areeasily identified by human observers. In this paper, we propose a novelapproach, called Adversarial Camouflage (\emph{AdvCam}), to craft andcamouflage physical-world adversarial examples into natural styles that appearlegitimate to human observers. Specifically, \emph{AdvCam} transfers largeadversarial perturbations into customized styles, which are then "hidden"on-target object or off-target background. Experimental evaluation shows that,in both digital and physical-world scenarios, adversarial examples crafted by\emph{AdvCam} are well camouflaged and highly stealthy, while remainingeffective in fooling state-of-the-art DNN image classifiers. Hence,\emph{AdvCam} is a flexible approach that can help craft stealthy attacks toevaluate the robustness of DNNs. \emph{AdvCam} can also be used to protectprivate information from being detected by deep learning systems.

Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles