发表机构
The Hong Kong Polytechnic University; The Education University of Hong Kong; Simon Fraser University（香港理工大学; 香港教育大学; 西蒙菲莎大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对图像传感器的电磁信号注入攻击，提出利用光学黑像素的轻量级检测方法，实现高ROC-AUC和低EER，无需硬件修改。
AI 中文摘要
电磁信号注入攻击（ESIA）对图像传感器构成日益严重的威胁，这些传感器越来越多地用于各种智能系统中。通过发射电磁干扰，攻击者可以操纵像素值，可能误导下游人工智能（AI）模型，并导致这些系统做出不安全决策。我们提出了一种轻量级检测方法，利用光学黑像素（即许多现代图像传感器中已存在的未曝光像素）来识别攻击。我们的检测方法在不同攻击条件下实现了高达99.6%的受试者工作特征曲线下面积（ROC-AUC）和低至0.027的等错误率（EER）。该方法需要极小的计算开销且无需硬件修改，使其成为保护基于视觉的系统免受ESIA攻击的一种实用且有效的防御手段。
英文摘要
Electromagnetic signal injection attacks (ESIA) pose a growing threat to image sensors, which are increasingly used in different intelligent systems. By emitting electromagnetic interference, adversaries can manipulate pixel values, potentially misleading downstream artificial intelligence (AI) models and causing unsafe decisions in these systems. We present a lightweight detection method that leverages optically black pixels, which are non-exposed pixels already present in many modern image sensors, to identify the attacks. Our detection approach achieves an area under the receiver operating characteristic curve (ROC-AUC) of up to 99.6\% and an Equal Error Rate (EER) as low as 0.027 across diverse attack conditions. Our method requires minimal computational overhead and no hardware modifications, making it a practical and effective defense for securing vision-based systems against ESIA.
Comments30 pages, 12 figures, 4 tables
Journal refThe 29th International Symposium on Research in Attacks, Intrusions and Defenses (RAID 2026)
