Intentional Electromagnetic Interference Attacks on Facial Recognition
Quick Answer
This paper reveals a vulnerability in facial recognition systems to intentional electromagnetic interference (EMI) using accessible RF equipment.
Quick Take
It assesses the robustness of current face recognition methods against such attacks and introduces a new dataset for benchmarking performance under EMI conditions.
Key Points
- Demonstrates EMI attacks on facial recognition using common RF equipment.
- Assesses state-of-the-art face recognition methods against RF-based interference.
- Introduces a dataset of facial images with and without EMI for benchmarking.
- Highlights a critical vulnerability in the physical-to-digital pipeline of biometric sensors.
- Aims to complement existing presentation attack testing standards.
Paper Resources
📖 Reader Mode
~2 min readAbstract:Attacks on general computer vision algorithms are often relegated to the digital domain, with the optimization performed purely in the digital world and then translated to physical mediums for implementation. In the field of biometrics, including facial recognition, physical presentation attacks targeting biometric sensors are dominant and present significant opportunity and risk. This paper highlights a critical vulnerability in the physical-to-digital pipeline of biometric sensors and provides a standardized approach for testing facial recognition system robustness against hardware attacks, going beyond and potentially complementing presentation attacks (as defined in ISO/IEC 30107 standard series). Specifically, in this work we (a) demonstrate that intentional electromagnetic interference is possible to be conducted with commonly accessible radio frequency (RF) equipment, (b) assess the robustness of state-of-the-art face recognition methods against RF-based attacks, and (c) provide a dataset composed of face images captured with and without electromagnetic interference to serve as a new benchmark for testing modern face matchers against RF-sourced interference.
| Comments: | To be published in IEEE/IAPR IJCB (International Joint Conference on Biometrics) 2026 |
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Cryptography and Security (cs.CR); Machine Learning (cs.LG) |
| Cite as: | arXiv:2607.15512 [cs.CV] |
| (or arXiv:2607.15512v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2607.15512 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Tyler Fitzsimmons [view email]
[v1]
Thu, 16 Jul 2026 23:55:15 UTC (13,233 KB)
— Originally published at arxiv.org
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