TY - GEN
T1 - Rainbow Artifacts from Electromagnetic Signal Injection Attacks on Image Sensors
AU - Zhang, Youqian
AU - Wang, Zhihao
AU - Ji, Xinyu
AU - Jiang, Qinhong
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/11
Y1 - 2025/11
N2 - Image sensors are integral to a wide range of safetyand security-critical systems, including surveillance infrastructure, autonomous vehicles, and industrial automation. These systems rely on the integrity of visual data to make decisions. In this work, we investigate a novel class of electromagnetic signal injection attacks that target the analog domain of image sensors, allowing adversaries to manipulate raw visual inputs without triggering conventional digital integrity checks. We uncover a previously undocumented attack phenomenon on CMOS image sensors: rainbow-like color artifacts induced in images captured by image sensors through carefully tuned electromagnetic interference. We further evaluate the impact of these attacks on state-of-the-art object detection models, showing that the injected artifacts propagate through the image signal processing pipeline and lead to significant mispredictions. Our findings highlight a critical and underexplored vulnerability in the visual perception stack, highlighting the need for more robust defenses against physical-layer attacks in such systems.
AB - Image sensors are integral to a wide range of safetyand security-critical systems, including surveillance infrastructure, autonomous vehicles, and industrial automation. These systems rely on the integrity of visual data to make decisions. In this work, we investigate a novel class of electromagnetic signal injection attacks that target the analog domain of image sensors, allowing adversaries to manipulate raw visual inputs without triggering conventional digital integrity checks. We uncover a previously undocumented attack phenomenon on CMOS image sensors: rainbow-like color artifacts induced in images captured by image sensors through carefully tuned electromagnetic interference. We further evaluate the impact of these attacks on state-of-the-art object detection models, showing that the injected artifacts propagate through the image signal processing pipeline and lead to significant mispredictions. Our findings highlight a critical and underexplored vulnerability in the visual perception stack, highlighting the need for more robust defenses against physical-layer attacks in such systems.
KW - Artificial Intelligence
KW - Computer Vision
KW - Electromagnetic Interference
KW - Image Sensor
KW - Object Detection
UR - https://www.scopus.com/pages/publications/105031730837
U2 - 10.1109/SiPS66314.2025.11261265
DO - 10.1109/SiPS66314.2025.11261265
M3 - Conference article published in proceeding or book
AN - SCOPUS:105031730837
T3 - 2025 IEEE Workshop on Signal Processing Systems, SiPS 2025
SP - 1
EP - 5
BT - 2025 IEEE Workshop on Signal Processing Systems, SiPS 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2025 IEEE Workshop on Signal Processing Systems, SiPS 2025
Y2 - 1 November 2025 through 4 November 2025
ER -