Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
| Autor: | Duan, Ranjie, Ma, Xingjun, Wang, Yisen, Bailey, James, Qin, A. K., Yang, Yun |
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| Rok vydání: | 2020 |
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| Druh dokumentu: | Working Paper |
| Popis: | 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. Comment: Accepted to CVPR2020 |
| Databáze: | arXiv |
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