AdLift: 将对抗扰动提升以保护3D高斯点云资产免受指令驱动编辑的影响
AdLift: Lifting Adversarial Perturbations to Safeguard 3D Gaussian Splatting Assets Against Instruction-Driven Editing
AI总结：
AdLift通过提升2D对抗扰动至3D高斯表示，有效保护3DGS资产免受指令驱动编辑攻击。
摘要：
近期研究将基于扩散的指令驱动2D图像编辑管道扩展到3D高斯点云（3DGS），使3DGS资产能够得到忠实的操控，并极大地推动了3DGS内容创作。然而，这也使这些资产面临严重的未经授权编辑和恶意篡改的风险。尽管针对扩散模型的不可察觉对抗扰动已被证明对保护2D图像有效，但将其应用于3DGS则面临两个主要挑战：视图通用性的保护和在隐身与保护能力之间的平衡。在本文中，我们提出了首个针对3DGS的编辑保护机制，称为AdLift，它通过将严格限定的2D对抗扰动提升到3D高斯表示的保护中，防止在任意视图和维度上的指令驱动编辑。为了确保对抗扰动的有效性和隐身性，这些保护高斯分布是在训练视图上逐步优化的，使用了定制的Lifted PGD。该方法首先在渲染图像中从编辑模型进行反向传播时进行梯度截断，并应用投影梯度以严格限制图像层面的扰动。然后，所得到的扰动通过图像到高斯拟合操作反向传播到保护高斯参数。我们交替进行梯度截断和图像到高斯拟合，从而在不同视图上获得一致的对抗性保护性能，并且可以推广到新的视图。实验结果表明，AdLift在对抗最先进的指令驱动2D图像和3DGS编辑方面效果显著。
英文摘要：
Recent studies have extended diffusion-based instruction-driven 2D image editing pipelines to 3D Gaussian Splatting (3DGS), enabling faithful manipulation of 3DGS assets and greatly advancing 3DGS content creation. However, it also exposes these assets to serious risks of unauthorized editing and malicious tampering. Although imperceptible adversarial perturbations against diffusion models have proven effective for protecting 2D images, applying them to 3DGS encounters two major challenges: view-generalizable protection and balancing invisibility with protection capability. In this work, we propose the first editing safeguard for 3DGS, termed AdLift, which prevents instruction-driven editing across arbitrary views and dimensions by lifting strictly bounded 2D adversarial perturbations into 3D Gaussian-represented safeguard. To ensure both adversarial perturbations effectiveness and invisibility, these safeguard Gaussians are progressively optimized across training views using a tailored Lifted PGD, which first conducts gradient truncation during back-propagation from the editing model at the rendered image and applies projected gradients to strictly constrain the image-level perturbation. Then, the resulting perturbation is backpropagated to the safeguard Gaussian parameters via an image-to-Gaussian fitting operation. We alternate between gradient truncation and image-to-Gaussian fitting, yielding consistent adversarial-based protection performance across different viewpoints and generalizes to novel views. Empirically, qualitative and quantitative results demonstrate that AdLift effectively protects against state-of-the-art instruction-driven 2D image and 3DGS editing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
