Intentional Electromagnetic Interference Attacks on Facial Recognition

2026-07-16

AI Summary

This study addresses a critical gap in face recognition security evaluation by introducing intentional electromagnetic interference (IEMI) as a novel physical threat during the image acquisition phase. Leveraging off-the-shelf radio-frequency equipment, the authors systematically assess the robustness of state-of-the-art face recognition algorithms under IEMI attacks conducted during standard face capture procedures. The work presents the first benchmark dataset comprising paired facial images captured with and without electromagnetic interference, which is publicly released to support further research. Experimental results demonstrate that IEMI significantly degrades recognition performance, exposing a previously overlooked vulnerability at the physical-to-digital interface of biometric systems. This approach establishes a new evaluation framework that extends beyond conventional presentation attacks, offering foundational insights for developing more resilient defenses in real-world biometric deployments.

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.

Problem

• Intentional Electromagnetic Interference
• Facial Recognition
• Biometric Sensors
• Hardware Attacks
• RF Interference

Innovation

• Intentional Electromagnetic Interference (IEMI)
• Facial Recognition Robustness
• RF-based Attacks
• Biometric Sensor Vulnerability
• Hardware Security