GuardMarkGS: 3D高斯溅射的统一所有权追溯与编辑威慑
GuardMarkGS: Unified Ownership Tracing and Edit Deterrence for 3D Gaussian Splatting
AI总结：
本文提出GuardMarkGS框架，通过结合全局水印目标与对抗目标，实现3D高斯溅射资产的统一所有权追溯与未经授权编辑的威慑，实验表明其在比特精度、编辑威慑和渲染质量间取得良好平衡。
摘要：
3D高斯溅射（3DGS）正成为新型视角合成的实用表示，但其快速增长的采用与指令驱动的3DGS编辑技术的快速进展，也暴露了双重版权风险：一旦3DGS资产发布，便可被未经授权使用并通过3D编辑修改。现有保护方法仅解决问题的一方面。水印可以追溯未经授权使用的所有权，但无法防止恶意编辑。对抗性编辑威慑方法可以干扰编辑，但不提供所有权证据。到目前为止，我们提出了首个针对3DGS的统一保护框架，同时优化所有权追溯和未经授权编辑的威慑。该框架结合了所有高斯的全局水印目标与对抗性编辑威慑目标。对抗分支结合了潜在锚点分离、去噪轨迹偏转和交叉注意力偏转，以偏转编辑轨迹，同时一个基于更新显著性的高斯选择策略将更强的对抗更新分配给掩码选择的高斯，提高水印恢复、编辑威慑和渲染保真度之间的平衡。在Mip-NeRF 360和Instruct-NeRF2NeRF场景上的实验表明，所提出的框架在比特精度、编辑威慑和渲染质量之间取得了良好的平衡。这些结果表明，通过将所有权追溯和未经授权编辑的威慑整合到单一优化框架中，可以更有效地解决3DGS资产的实用版权保护问题。
英文摘要：
3D Gaussian Splatting (3DGS) is becoming a practical representation for novel view synthesis, but its growing adoption, together with rapid advances in instruction-driven 3DGS editing, also exposes a dual copyright risk: once a 3DGS-based asset is released, it can be used without permission and manipulated through 3D editing. Existing protection methods address only one side of this problem. Watermarking can trace ownership after unauthorized use, but it cannot prevent malicious editing. Adversarial edit-deterrence methods can disrupt editing, but they do not provide evidence of ownership. To the best of our knowledge, we present the first unified protection framework for 3DGS that jointly optimizes ownership tracing and unauthorized editing deterrence. Our framework combines a scene-wide watermarking objective over all Gaussians with an adversarial objective for edit deterrence. The adversarial branch combines latent-anchor separation, denoising-trajectory diversion, and cross-attention diversion to divert the editing trajectory, while an update-saliency-motivated Gaussian selection strategy assigns stronger adversarial updates to mask-selected Gaussians, improving the balance among watermark recovery, edit deterrence, and rendering fidelity. Experiments on scenes from Mip-NeRF 360 and Instruct-NeRF2NeRF demonstrate that the proposed framework achieves a favorable balance among bit accuracy, edit deterrence, and rendering quality. These results suggest that practical copyright protection of 3DGS-based assets can be more effectively addressed by integrating ownership tracing and unauthorized editing deterrence into a single optimization framework.
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。
