{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:44:13Z","timestamp":1787017453040,"version":"build-2736575974"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"6","license":[{"start":{"date-parts":[[2024,11,23]],"date-time":"2024-11-23T00:00:00Z","timestamp":1732320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Funds for Creative Research Groups of China","award":["61921003"],"award-info":[{"award-number":["61921003"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62225204 and 61932013"],"award-info":[{"award-number":["62225204 and 61932013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"A3 Foresight Program of the NSFC","award":["62061146002"],"award-info":[{"award-number":["62061146002"]}]},{"name":"Xiaomi Young Talents Program of the Xiaomi Foundation"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Sen. Netw."],"published-print":{"date-parts":[[2024,11,30]]},"abstract":"<jats:p>\n                    With the widespread adoption of deep learning models in wireless sensing, substantial efforts have been made to develop sophisticated models that improve the accuracy and performance of sensing applications. However, the exploration of potential vulnerabilities in deep learning models has been limited, with existing studies primarily focusing on evaluating wireless adversarial performance in communication or sensing alone. Moreover, there is a lack of a comprehensive definition for attack imperceptibility. In this article, we come up with a definition of the wireless attack\n                    <jats:italic>imperceptibility<\/jats:italic>\n                    for both communication and sensing. Our objective is to create an adversarial perturbation capable of degrading WiFi sensing performance while preserving WiFi communication integrity. To achieve this, we propose\n                    <jats:bold>WiCAM2.0<\/jats:bold>\n                    to reveal the temporal and spatial attention of a deep neural network, capturing the crucial portions of its input. Then, we design a mask to confine adversarial perturbations in the attended parts only, minimizing the impact on WiFi communication. WiCAM2.0 is a general adversarial framework that integrates adversarial methods such as the Fast Gradient Sign Method and Projected Gradient Descent to generate perturbations, capable of initiating both non-targeted and targeted attacks. We carry out experiments on three popular WiFi sensing applications, including human activity recognition, gesture recognition, and user identification. Extensive experiments are conducted on both public datasets and self-collected datasets.\n                  <\/jats:p>","DOI":"10.1145\/3698592","type":"journal-article","created":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T07:00:39Z","timestamp":1728284439000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["WiCAM2.0: Imperceptible and Targeted Attack on Deep Learning based WiFi Sensing"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-8707-5942","authenticated-orcid":false,"given":"Leiyang","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7950-6773","authenticated-orcid":false,"given":"Xiaolong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-5224-9794","authenticated-orcid":false,"given":"Yucheng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5040-2468","authenticated-orcid":false,"given":"Liang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7199-5047","authenticated-orcid":false,"given":"Huadong","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science, Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,11,23]]},"reference":[{"key":"e_1_3_1_2_2","volume-title":"Proceedings of ICLR","author":"Goodfellow Ian J.","year":"2015","unstructured":"Ian J. 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Two-stream convolution augmented transformer for human activity recognition. In Proceedings of AAAI. 286\u2013293."},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1700143"},{"key":"e_1_3_1_8_2","volume-title":"Proceedings of IEEE INFOCOM","author":"Zheng Xiaolong","year":"2016","unstructured":"Xiaolong Zheng, Jiliang Wang, Longfei Shangguan, Zimu Zhou, and Yunhao Liu. 2016. Smokey: Ubiquitous smoking detection with commercial WiFi infrastructures. 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Wilmg: Pushing the limit of WiFi sensing with low transmission rates. In Proceedings of IEEE SECON. IEEE, 1\u20139."},{"key":"e_1_3_1_14_2","article-title":"Pushing the limits of WiFi sensing with low transmission rates","author":"Zheng Xiaolong","year":"2024","unstructured":"Xiaolong Zheng, Kun Yang, Jie Xiong, Liang Liu, and Huadong Ma. 2024. Pushing the limits of WiFi sensing with low transmission rates. IEEE Transactions on Mobile Computing (2024), 1\u201315.","journal-title":"IEEE Transactions on Mobile Computing"},{"key":"e_1_3_1_15_2","article-title":"Wireless sensing for material identification: A survey","author":"Chen Yande","year":"2024","unstructured":"Yande Chen, Chongzhi Xu, Kexin Li, Jia Zhang, Xiuzhen Guo, Meng Jin, Xiaolong Zheng, and Yuan He. 2024. Wireless sensing for material identification: A survey. 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