发表机构
University of Notre Dame; Naval Surface Warfare Center, Crane Division（圣母大学; 海军水面作战中心克兰分部）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究面部识别中针对生物识别传感器物理到数字管道漏洞的攻击。核心方法是用常见射频设备进行有意电磁干扰，评估先进人脸识别方法的鲁棒性，并提供相关数据集。主要贡献是提供测试面部识别系统抗硬件攻击的标准化方法及新基准。
AI 中文摘要
对通用计算机视觉算法的攻击通常局限于数字领域，在数字世界中进行优化后再转换到物理介质中实施。在生物识别领域，包括面部识别，针对生物识别传感器的物理呈现攻击占主导地位，存在重大机遇和风险。本文强调了生物识别传感器从物理到数字管道中的一个关键漏洞，并提供了一种标准化方法来测试面部识别系统对硬件攻击的鲁棒性，超越并可能补充呈现攻击（如ISO/IEC 30107标准系列所定义）。具体而言，我们（a）证明使用常见的射频（RF）设备可以进行有意电磁干扰，（b）评估了先进人脸识别方法对基于RF攻击的鲁棒性，（c）提供了一个由有和没有电磁干扰时捕获的面部图像组成的数据集，作为测试现代面部匹配器抗RF源干扰的新基准。
英文摘要
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.
CommentsTo be published in IEEE/IAPR IJCB (International Joint Conference on Biometrics) 2026
