  This study introduces a novel approach to neural rendering, specifically
tailored for adversarial camouflage, within an extensive 3D rendering
framework. Our method, named FPA, goes beyond traditional techniques by
faithfully simulating lighting conditions and material variations, ensuring a
nuanced and realistic representation of textures on a 3D target. To achieve
this, we employ a generative approach that learns adversarial patterns from a
diffusion model. This involves incorporating a specially designed adversarial
loss and covert constraint loss to guarantee the adversarial and covert nature
of the camouflage in the physical world. Furthermore, we showcase the
effectiveness of the proposed camouflage in sticker mode, demonstrating its
ability to cover the target without compromising adversarial information.
Through empirical and physical experiments, FPA exhibits strong performance in
terms of attack success rate and transferability. Additionally, the designed
sticker-mode camouflage, coupled with a concealment constraint, adapts to the
environment, yielding diverse styles of texture. Our findings highlight the
versatility and efficacy of the FPA approach in adversarial camouflage
applications.
