  Infrared physical adversarial examples are of great significance for studying
the security of infrared AI systems that are widely used in our lives such as
autonomous driving. Previous infrared physical attacks mainly focused on 2D
infrared pedestrian detection which may not fully manifest its destructiveness
to AI systems. In this work, we propose a physical attack method against
infrared detectors based on 3D modeling, which is applied to a real car. The
goal is to design a set of infrared adversarial stickers to make cars invisible
to infrared detectors at various viewing angles, distances, and scenes. We
build a 3D infrared car model with real infrared characteristics and propose an
infrared adversarial pattern generation method based on 3D mesh shadow. We
propose a 3D control points-based mesh smoothing algorithm and use a set of
smoothness loss functions to enhance the smoothness of adversarial meshes and
facilitate the sticker implementation. Besides, We designed the aluminum
stickers and conducted physical experiments on two real Mercedes-Benz A200L
cars. Our adversarial stickers hid the cars from Faster RCNN, an object
detector, at various viewing angles, distances, and scenes. The attack success
rate (ASR) was 91.49% for real cars. In comparison, the ASRs of random stickers
and no sticker were only 6.21% and 0.66%, respectively. In addition, the ASRs
of the designed stickers against six unseen object detectors such as YOLOv3 and
Deformable DETR were between 73.35%-95.80%, showing good transferability of the
attack performance across detectors.
