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.

翻译：近期研究将基于扩散模型的指令驱动二维图像编辑流程扩展至三维高斯泼溅（3DGS）领域，实现了对3DGS资产的精准操控，极大推动了3DGS内容创作的发展。然而，这也使得这些资产面临未经授权编辑与恶意篡改的严重风险。尽管针对扩散模型的不可感知对抗性扰动已被证明能有效保护二维图像，但将其应用于3DGS时面临两大挑战：视角通用化保护以及隐形性与防护能力的平衡。本研究提出首个面向3DGS的编辑防护方案AdLift，通过将严格有界的二维对抗性扰动提升至三维高斯表征的防护层，从而阻止任意视角与维度的指令驱动编辑。为确保对抗性扰动的有效性与不可见性，这些防护高斯体通过定制的Lifted PGD算法在训练视角间渐进优化：该算法首先在渲染图像层面执行编辑模型反向传播时的梯度截断，并应用投影梯度严格约束图像级扰动；随后通过图像到高斯拟合操作将生成的扰动反向传播至防护高斯参数。通过交替进行梯度截断与图像到高斯拟合，实现了跨不同视角的一致性对抗防护性能，并能泛化至新视角。实证研究表明，定性与定量结果均证明AdLift能有效抵御最先进的指令驱动二维图像及3DGS编辑攻击。