Yin, Guolin 
ORCID: 0009-0004-4031-8547, Zhang, Junqing ORCID: 0000-0002-3502-2926, Yi, Xinping ORCID: 0000-0001-5163-2364 and Wang, Xuyu ORCID: 0000-0002-4759-8674
(2025)
Evasion Attacks and Countermeasures in Deep Learning-Based Wi-Fi Gesture Recognition.
IEEE Transactions on Mobile Computing, 24 (9).
pp. 8180-8195.
ISSN 1536-1233, 1558-0660
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TMC2025_WiFi_Sensing_Adversarial_Attack.pdf - Author Accepted Manuscript Available under License Creative Commons Attribution. Download (1MB) | Preview |
Abstract
Deep learning-based Wi-Fi sensing has received massive interest thanks to the prevalence of Wi-Fi technology. While deep learning techniques provide promising results in Wi-Fi sensing, there are only very few studies on the vulnerabilities against Wi-Fi ensing. In this paper, we studied evasion attacks against deep learning-based Wi-Fi sensing and the countermeasure and conducted an extensive experimental evaluation using two publicly available datasets, namely SignFi and Widar. Accordingly, we proposed three white-box and two black-box attacks and revealed that even with an undetectable power change, evasion attacks can achieve a remarkable attack success rate (ASR) of 97.0% and 95.6% in white-box and black-box settings, respectively. These results highlight the urgent need for countermeasures against evasion attacks in Wi-Fi sensing systems. We introduced adversarial training and randomised smoothing, which notably improved the robustness of the Wi-Fi sensing model. The ASRs for white-box and black-box attacks were reduced to a minimum of around 6% and 2%, respectively. Moreover, randomised smoothing also introduced certifiable robustness, achieving 70.1% of samples certified for our model. The certification method provides an additional layer of reliability, ensuring that the model’s performance remains consistent and predictable even under adversarial conditions.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 4605 Data Management and Data Science, 46 Information and Computing Sciences, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD) |
| Divisions: | Faculty of Science and Engineering Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 31 Mar 2025 08:33 |
| Last Modified: | 09 Sep 2025 22:32 |
| DOI: | 10.1109/tmc.2025.3557757 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3191078 |
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