Intentional Electromagnetic Interference Attacks on Facial Recognition

Official GitHub repository for the paper: Tyler Fitzsimmons and Adam Czajka, "Intentional Electromagnetic Interference Attacks on Facial Recognition," IEEE/IAPR International Joint Conference on Biometrics, Rome, Italy, September 1-4, 2026 (ArXiv | IEEEXplore)

Table of contents

• Abstract
• Datasets
• Model Script
• Citation
• Acknowledgment

📌 Abstract

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.

📀 Datasets

IEMI Attack Dataset

We provide a new dataset of face videos representing 50 identities, recaptured from MBGC (Multiple Biometric Grand Challenge) V2, by a camera under the IEMI attack along with recaptured videos without the IEMI attack, to serve as a new benchmark for testing reliability of face recognition models. There are five folders contained in the dataset: Baseline Identities, Cropped Identities, Attack Videos Digital, Attack Videos Physical, and Impostor Distributions. If desired, please acquire the original MBGC v2 dataset for full testing purposes.

(a) Original MBGC v2 Image, (b) Clean Image, (c) Modeled Attack, (d) Physical Attack

Obtaining Copies of the Datasets

Researchers interested in obtaining a copy of the data associated with the paper are requested to execute the data sharing license agreement. Note for university licensees: We cannot accept licenses signed by students or postdoctoral scholars under any circumstances. We cannot accept licenses signed by faculty members unless they have been explicitly delegated the authority to make contracts on behalf of the institution. Your institution's legal or contracting office must review and execute the license.

🚀 IEMI Modeling Script

• Linux / Windows / macOS
• Python 3.9+
• CUDA (optional)

There are only two scripts necessary for the IEMI Modeling.

• RF_Optimization.py: Runs a grid search over provided parameters to determine theoretically optimal RF settings per model (models built in same as IJCB paper).

• RF_effect_modeling: Using the outputs from RF_Optimization.py, the script overlays the desired RF parameters (frequency, amplitude, bar angle, AM effect, FM effect) onto a directory of images.

It should also be noted the modeling is not intended to be an optimized adversarial attack. There are no machine learning models included in the modeling script. The goal is to simply generate images that have a level of qualitative disruption to provide a narrowed focus for the real-world physical attacks.

Citation

If you find this work useful in your research, please cite the following paper:

@inproceedings{fitzsimmonsIJCB2026,
      title={Intentional Electromagnetic Interference Attacks on Facial Recognition}, 
      author={Tyler Fitzsimmons and Adam Czajka},
      year={2026},
      booktitle={IEEE/IAPR International Joint Conference on Biometrics, Rome, Italy, September 1-4, 2026},
}

Acknowledgment

This material is based upon work partially supported by the OUSW/R&E (Office of the Under Secretary of War, Research and Engineering), National Defense Education Program (NDEP) SMART Scholarship Program, and Naval Surface Warfare Center (NSWC), Crane Division Ph.D. Fellowship Program. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the DoW or U.S. Navy.