# Towards Trustworthy Wi-Fi CSI-based Sensing: Systematic Evaluation of Adversarial Robustness

arXiv: https://arxiv.org/abs/2511.20456 · HTML: https://mlsanity.com/paper/2511.20456/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 30 of its 77 references that are in the map.
- First posted on arXiv 25 Nov 2025.

## Builds on (most cited first, 30 of 30)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks](https://mlsanity.com/paper/2003.01690/) (2020)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Provable defenses against adversarial examples via the convex outer adversarial polytope](https://mlsanity.com/paper/1711.00851/) (2017)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [Consistency Regularization for Adversarial Robustness](https://mlsanity.com/paper/2103.04623/) (2021)
- [EfficientFi: Towards Large-Scale Lightweight WiFi Sensing via CSI Compression](https://mlsanity.com/paper/2204.04138/) (2022)
- [SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing](https://mlsanity.com/paper/2207.07859/) (2022)
- [SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points](https://mlsanity.com/paper/2103.09924/) (2021)
- [AirFi: Empowering WiFi-based Passive Human Gesture Recognition to Unseen Environment via Domain Generalization](https://mlsanity.com/paper/2209.10285/) (2022)
- [Deep Learning for Radio-based Human Sensing: Recent Advances and Future Directions](https://mlsanity.com/paper/2010.12717/) (2020)
- [An Overview on IEEE 802.11bf: WLAN Sensing](https://mlsanity.com/paper/2310.17661/) (2023)
- [CrossFi: A Cross Domain Wi-Fi Sensing Framework Based on Siamese Network](https://mlsanity.com/paper/2408.10919/) (2024)
- [CSI-Bench: A Large-Scale In-the-Wild Dataset for Multi-task WiFi Sensing](https://mlsanity.com/paper/2505.21866/) (2025)
- [Adversarial Attacks on Time-Series Intrusion Detection for Industrial Control Systems](https://mlsanity.com/paper/1911.04278/) (2019)
- [LiteHAR: Lightweight Human Activity Recognition from WiFi Signals with Random Convolution Kernels](https://mlsanity.com/paper/2201.09310/) (2022)
- [SecureSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition](https://mlsanity.com/paper/2204.01560/) (2022)
- [Security Analysis of WiFi-based Sensing Systems: Threats from Perturbation Attacks](https://mlsanity.com/paper/2404.15587/) (2024)
- [FreeSense:Indoor Human Identification with WiFi Signals](https://mlsanity.com/paper/1608.03430/) (2016)
- [Integrated Sensing, Communication, and Computation for Over-the-Air Federated Edge Learning](https://mlsanity.com/paper/2508.15185/) (2025)
- [Et Tu Alexa? When Commodity WiFi Devices Turn into Adversarial Motion Sensors](https://mlsanity.com/paper/1810.10109/) (2018)
- [Over-The-Air Adversarial Attacks on Deep Learning Wi-Fi Fingerprinting](https://mlsanity.com/paper/2301.03760/) (2023)
- [KNN-MMD: Cross Domain Wireless Sensing via Local Distribution Alignment](https://mlsanity.com/paper/2412.04783/) (2024)
- [Countering Adversarial Examples: Combining Input Transformation and Noisy Training](https://mlsanity.com/paper/2106.13394/) (2021)
- [SoK: Secure Human-centered Wireless Sensing](https://mlsanity.com/paper/2211.12087/) (2022)
- [A Survey of Wireless Sensing Security from a Role-Based View: Victim, Weapon, and Shield](https://mlsanity.com/paper/2412.03064/) (2024)
- [Obfuscation of Human Micro-Doppler Signatures in Passive Wireless RADAR](https://mlsanity.com/paper/2303.03001/) (2023)
- [Wi-Fi Sensing Tool Release: Gathering 802.11ax Channel State Information from a Commercial Wi-Fi Access Point](https://mlsanity.com/paper/2506.16957/) (2025)

## Abstract (arXiv)

Machine learning drives Channel State Information (CSI)-based human sensing in modern wireless networks, enabling applications like device-free human activity recognition (HAR) and identification (HID). However, the susceptibility of these models to adversarial perturbations raises security concerns that must be quantified prior to edge deployment. We present a systematic robustness evaluation of five diverse CSI architectures across four public datasets, jointly analyzing white-box, black-box transfer, and universal attacks, together with defense strategies, under unconstrained and physics-guided perturbation boundaries. Contrary to prior assumptions, our experiments reveal that model capacity does not guarantee robustness; simple architectures consistently exhibit superior resilience compared to high-capacity sequence and vision models. Furthermore, vulnerability is fundamentally task-dependent, with HAR proving highly susceptible to attack, while HID demonstrates stark inherent resistance. Crucially, enforcing physical signal constraints drastically reduces attack success rates and significantly taxes attacker computation, showing that standard unconstrained feature-space attacks substantially overestimate real-world Over-The-Air vulnerabilities. By synthesizing attack, defense, and security metrics with strict edge hardware considerations, this work establishes foundational design principles for secure, deployable, and physically realizable wireless sensing systems.