# Rainbow Artifacts from Electromagnetic Signal Injection Attacks on Image Sensors

arXiv: https://arxiv.org/abs/2507.07773 · HTML: https://mlsanity.com/paper/2507.07773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 10 Jul 2025.

## Builds on (most cited first, 9 of 9)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [YOLOv10: Real-Time End-to-End Object Detection](https://mlsanity.com/paper/2405.14458/) (2024)
- [Signal Injection Attacks against CCD Image Sensors](https://mlsanity.com/paper/2108.08881/) (2021)
- [Understanding Impacts of Electromagnetic Signal Injection Attacks on Object Detection](https://mlsanity.com/paper/2407.16327/) (2024)
- [Is Your Autonomous Vehicle Safe? Understanding the Threat of Electromagnetic Signal Injection Attacks on Traffic Scene Perception](https://mlsanity.com/paper/2501.05239/) (2025)
- [A Simulation Framework for Electromagnetic Signal Injection Attacks on Image Sensors](https://mlsanity.com/paper/2408.05124/) (2024)
- [Detection of Electromagnetic Signal Injection Attacks on Actuator Systems](https://mlsanity.com/paper/2203.07102/) (2022)

## Built on by (most cited first, 1 of 1)

- [Intentional Electromagnetic Interference Attacks on Facial Recognition](https://mlsanity.com/paper/2607.15512/) (2026)

## Abstract (arXiv)

Image sensors are integral to a wide range of safety- and 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.