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.

翻译：本研究提出了一种新颖的神经渲染方法，专门针对对抗性伪装设计，并在大规模三维渲染框架内实现。我们的方法命名为FPA，超越了传统技术，通过精确模拟光照条件和材料变化，确保三维目标上纹理的细致且逼真再现。为实现这一目标，我们采用生成式方法，从扩散模型中学习对抗模式，并引入专门设计的对抗损失和隐蔽约束损失，以保证伪装在物理世界中兼具对抗性与隐蔽性。此外，我们展示了所提出伪装以贴纸模式运行的有效性，证明其在覆盖目标的同时不削弱对抗信息的能力。通过实证和物理实验，FPA在攻击成功率和可迁移性方面表现出色。同时，结合隐蔽约束设计的贴纸模式伪装能够适应环境，产生多样化的纹理风格。我们的发现突出了FPA方法在对抗性伪装应用中的多功能性和有效性。