{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:52:35Z","timestamp":1783439555616,"version":"3.54.6"},"reference-count":45,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2022,7,4]],"date-time":"2022-07-04T00:00:00Z","timestamp":1656892800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2022,7,4]]},"abstract":"<jats:p>WiFi-based gesture recognition systems have attracted enormous interest owing to the non-intrusive of WiFi signals and the wide adoption of WiFi for communication. Despite boosted performance via integrating advanced deep neural network (DNN) classifiers, there lacks sufficient investigation on their security vulnerabilities, which are rooted in the open nature of the wireless medium and the inherent defects (e.g., adversarial attacks) of classifiers. To fill this gap, we aim to study adversarial attacks to DNN-powered WiFi-based gesture recognition to encourage proper countermeasures. We design WiAdv to construct physically realizable adversarial examples to fool these systems. WiAdv features a signal synthesis scheme to craft adversarial signals with desired motion features based on the fundamental principle of WiFi-based gesture recognition, and a black-box attack scheme to handle the inconsistency between the perturbation space and the input space of the classifier caused by the in-between non-differentiable processing modules. We realize and evaluate our attack strategies against a representative state-of-the-art system, Widar3.0 in realistic settings. The experimental results show that the adversarial wireless signals generated by WiAdv achieve over 70% attack success rate on average, and remain robust and effective across different physical settings. Our attack case study and analysis reveal the vulnerability of WiFi-based gesture recognition systems, and we hope WiAdv could help promote the improvement of the relevant systems.<\/jats:p>","DOI":"10.1145\/3534618","type":"journal-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T18:50:18Z","timestamp":1657219818000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":28,"title":["WiAdv"],"prefix":"10.1145","volume":"6","author":[{"given":"Yuxuan","family":"Zhou","sequence":"first","affiliation":[{"name":"CSE, Hong Kong University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huangxun","family":"Chen","sequence":"additional","affiliation":[{"name":"Huawei Theory Lab, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyu","family":"Huang","sequence":"additional","affiliation":[{"name":"Tencent, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"CSE, Hong Kong University of Science and Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2015.7218525"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2486001.2486039"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"283","author":"Athalye Anish","year":"2018","unstructured":"Anish Athalye, Nicholas Carlini, and David Wagner. 2018. Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80), Jennifer Dy and Andreas Krause (Eds.). PMLR, 274--283. https:\/\/proceedings.mlr.press\/v80\/athalye18a.html"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0090256"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01258-8_10"},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2740070.2626327"},{"key":"e_1_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397332"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3319535.3339815"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790114"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP40001.2021.00004"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFCOM.2007.228"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00872"},{"key":"e_1_2_1_14_1","volume-title":"accessed","author":"Radio Website GNU","year":"2012","unstructured":"GNU Radio Website. accessed February 2012. http:\/\/www.gnuradio.org"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","unstructured":"Ian J. Goodfellow Jonathon Shlens and Christian Szegedy. 2014. Explaining and Harnessing Adversarial Examples. https:\/\/doi.org\/10.48550\/ARXIV.1412.6572","DOI":"10.48550\/ARXIV.1412.6572"},{"key":"e_1_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1925861.1925870"},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241548"},{"key":"e_1_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2009.4959977"},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2906388.2906401"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPSN.2005.1440903"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2897824.2925953"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191755"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3131897"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cviu.2020.103111"},{"key":"e_1_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2500423.2500436"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/3084041.3084067"},{"key":"e_1_2_1_28_1","volume-title":"13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16)","author":"Qiao Yue","year":"2016","unstructured":"Yue Qiao, Ouyang Zhang, Wenjie Zhou, Kannan Srinivasan, and Anish Arora. 2016. PhyCloak: Obfuscating Sensing from Communication Signals. In 13th USENIX Symposium on Networked Systems Design and Implementation (NSDI 16). USENIX Association, Santa Clara, CA, 685--699. https:\/\/www.usenix.org\/conference\/nsdi16\/technical-sessions\/presentation\/qiao"},{"key":"e_1_2_1_29_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"5240","author":"Qin Yao","year":"2019","unstructured":"Yao Qin, Nicholas Carlini, Garrison Cottrell, Ian Goodfellow, and Colin Raffel. 2019. Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 5231--5240. https:\/\/proceedings.mlr.press\/v97\/qin19a.html"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1103"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351261"},{"key":"e_1_2_1_32_1","volume-title":"Hsiao-Chun Wu, Scott C-H Huang, and Hsiao-Hwa Chen.","author":"Shiu Yi-Sheng","year":"2011","unstructured":"Yi-Sheng Shiu, Shih Yu Chang, Hsiao-Chun Wu, Scott C-H Huang, and Hsiao-Hwa Chen. 2011. Physical layer security in wireless networks: A tutorial. IEEE wireless Communications 18, 2 (2011), 66--74."},{"key":"e_1_2_1_33_1","unstructured":"William C Stone et al. 1997. Electromagnetic signal attenuation in construction materials. (1997)."},{"key":"e_1_2_1_34_1","volume-title":"Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures. In 29th USENIX Security Symposium (USENIX Security 20)","author":"Sun Jiachen","unstructured":"Jiachen Sun, Yulong Cao, Qi Alfred Chen, and Z. Morley Mao. 2020. Towards Robust LiDAR-based Perception in Autonomous Driving: General Black-box Adversarial Sensor Attack and Countermeasures. In 29th USENIX Security Symposium (USENIX Security 20). USENIX Association, 877--894. https:\/\/www.usenix.org\/conference\/usenixsecurity20\/presentation\/sun"},{"key":"e_1_2_1_35_1","volume-title":"Intriguing properties of neural networks. arXiv:1312.6199 [cs] (Feb","author":"Szegedy Christian","year":"2014","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. arXiv:1312.6199 [cs] (Feb. 2014). http:\/\/arxiv.org\/abs\/1312.6199 arXiv: 1312.6199."},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.01373"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3210240.3210335"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.783"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/2639108.2639143"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2820716.2820727"},{"key":"e_1_2_1_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2370216.2370269"},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3241539.3241570"},{"key":"e_1_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3105387"},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3307334.3326081"},{"key":"e_1_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2020.23053"}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534618","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534618","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,14]],"date-time":"2025-07-14T04:30:55Z","timestamp":1752467455000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534618"}},"subtitle":["Practical and Robust Adversarial Attack against WiFi-based Gesture Recognition System"],"short-title":[],"issued":{"date-parts":[[2022,7,4]]},"references-count":45,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2022,7,4]]}},"alternative-id":["10.1145\/3534618"],"URL":"https:\/\/doi.org\/10.1145\/3534618","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,4]]},"assertion":[{"value":"2022-07-07","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}