Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World

TL;DR

This paper introduces infrared adversarial clothing with QR code patterns that effectively evade infrared detectors in both digital simulations and real-world tests, enhancing privacy and security.

Contribution

It presents a novel design of adversarial clothing using QR code patterns that maintain effectiveness after deformation and can be manufactured into real garments with new materials.

Findings

Optimized QR code pattern reduces YOLOv3 detection AP by 87.7% digitally.

Physical experiments show a 64.6% AP reduction in real-world scenarios.

Model ensemble improves attack transferability to unseen detectors.

Abstract

Thermal infrared imaging is widely used in body temperature measurement, security monitoring, and so on, but its safety research attracted attention only in recent years. We proposed the infrared adversarial clothing, which could fool infrared pedestrian detectors at different angles. We simulated the process from cloth to clothing in the digital world and then designed the adversarial "QR code" pattern. The core of our method is to design a basic pattern that can be expanded periodically, and make the pattern after random cropping and deformation still have an adversarial effect, then we can process the flat cloth with an adversarial pattern into any 3D clothes. The results showed that the optimized "QR code" pattern lowered the Average Precision (AP) of YOLOv3 by 87.7%, while the random "QR code" pattern and blank pattern lowered the AP of YOLOv3 by 57.9% and 30.1%, respectively, in the digital world. We then manufactured an adversarial shirt with a new material: aerogel. Physical-world experiments showed that the adversarial "QR code" pattern clothing lowered the AP of YOLOv3 by 64.6%, while the random "QR code" pattern clothing and fully heat-insulated clothing lowered the AP of YOLOv3 by 28.3% and 22.8%, respectively. We used the model ensemble technique to improve the attack transferability to unseen models.