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.

本研究介绍了一种新颖的神经渲染方法，特别适用于对抗性伪装，采用广泛的三维渲染框架。我们的方法名为 FPA，通过忠实地模拟光照条件和材质变化，确保对三维目标纹理的细致而逼真的表达。为了实现这一目标，我们采用了一种生成方法，从扩散模型中学习对抗性的模式。这涉及到结合特殊设计的对抗性损失和隐蔽约束损失，以确保物理世界中伪装的对抗性和隐蔽性。此外，我们展示了所提出的伪装在贴纸模式下的有效性，证明了其能够在不损害对抗性信息的情况下覆盖目标。通过实证和物理实验，FPA 在攻击成功率和可转移性方面展现出强大的性能。此外，设计的贴纸模式伪装结合隐蔽约束可以适应环境，产生多样的纹理风格。我们的研究结果突出了 FPA 方法在对抗性伪装应用中的多样性和功效。