{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/adversarial-camouflage-hiding-physical-world","title":"Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles","arxiv_id":"2003.08757","date":"2020-03-08","proceeding":"CVPR 2020 6","authors":["Ranjie Duan","Xingjun Ma","Yisen Wang","James Bailey","A. K. Qin","Yun Yang"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2003.08757v2","url_pdf":"https://arxiv.org/pdf/2003.08757v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"adversarial-camouflage-hiding-physical-world","repo_url":"https://github.com/RjDuan/AdvCam-Hide-Adv-with-Natural-Styles","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2003.08757","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}