Physically Realizable Natural-Looking Clothing Textures Evade Person
  Detectors via 3D Modeling
Zhanhao Hu Wenda Chu Xiaopei Zhu Hui Zhang Bo Zhang Xiaolin Hu
Recent works have proposed to craft adversarial clothes for evading person
detectors, while they are either only effective at limited viewing angles or
very conspicuous to humans. We aim to craft adversarial texture for clothes
based on 3D modeling, an idea that has been used to craft rigid adversarial
objects such as a 3D-printed turtle. Unlike rigid objects, humans and clothes
are non-rigid, leading to difficulties in physical realization. In order to
craft natural-looking adversarial clothes that can evade person detectors at
multiple viewing angles, we propose adversarial camouflage textures (AdvCaT)
that resemble one kind of the typical textures of daily clothes, camouflage
textures. We leverage the Voronoi diagram and Gumbel-softmax trick to
parameterize the camouflage textures and optimize the parameters via 3D
modeling. Moreover, we propose an efficient augmentation pipeline on 3D meshes
combining topologically plausible projection (TopoProj) and Thin Plate Spline
(TPS) to narrow the gap between digital and real-world objects. We printed the
developed 3D texture pieces on fabric materials and tailored them into T-shirts
and trousers. Experiments show high attack success rates of these clothes
against multiple detectors.