  Deep neural networks (DNNs) are known to be vulnerable to adversarial
examples. Existing works have mostly focused on either digital adversarial
examples created via small and imperceptible perturbations, or physical-world
adversarial examples created with large and less realistic distortions that are
easily identified by human observers. In this paper, we propose a novel
approach, called Adversarial Camouflage (\emph{AdvCam}), to craft and
camouflage physical-world adversarial examples into natural styles that appear
legitimate to human observers. Specifically, \emph{AdvCam} transfers large
adversarial 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 remaining
effective in fooling state-of-the-art DNN image classifiers. Hence,
\emph{AdvCam} is a flexible approach that can help craft stealthy attacks to
evaluate the robustness of DNNs. \emph{AdvCam} can also be used to protect
private information from being detected by deep learning systems.
