何处、何物、为何：迈向可解释的3D-GS水印
Where, What, Why: Toward Explainable 3D-GS Watermarking
AI总结：
本文提出一种原生表示框架，通过三专家模块和安全预算感知门实现水印载体选择和质量保护，结合通道组掩码提升水印鲁棒性和可解释性，相比现有方法在PSNR和比特精度上均有提升。
摘要：
随着3D高斯散射成为交互式3D资产的主流表示形式，鲁棒且不可察觉的水印技术至关重要。我们提出一种原生表示框架，将写入位置与质量保护分离。三专家模块直接在高斯原语上推导载体选择先验，安全预算感知门（SBAG）将高斯分配到水印载体，优化扰动下的位鲁棒性和比特率预算，同时优化视觉补偿器以避免水印损失。为保持保真度，我们引入通道组掩码控制载体和补偿器的梯度传播，从而限制高斯参数更新，修复局部伪影，保持高频细节而不增加运行时间。我们的设计实现了视图一致的水印持久性和对常见图像失真（如压缩和噪声）的强鲁棒性，同时在鲁棒性与质量之间取得优于先前方法的平衡。此外，解耦微调提供每高斯的属性，揭示信息承载位置及为何选择这些载体，实现可审计的可解释性。与现有最先进方法相比，我们的方法在PSNR上提高了+0.83 dB，在比特精度上提高了+1.24%。
英文摘要：
As 3D Gaussian Splatting becomes the de facto representation for interactive 3D assets, robust yet imperceptible watermarking is critical. We present a representation-native framework that separates where to write from how to preserve quality. A Trio-Experts module operates directly on Gaussian primitives to derive priors for carrier selection, while a Safety and Budget Aware Gate (SBAG) allocates Gaussians to watermark carriers, optimized for bit resilience under perturbation and bitrate budgets, and to visual compensators that are insulated from watermark loss. To maintain fidelity, we introduce a channel-wise group mask that controls gradient propagation for carriers and compensators, thereby limiting Gaussian parameter updates, repairing local artifacts, and preserving high-frequency details without increasing runtime. Our design yields view-consistent watermark persistence and strong robustness against common image distortions such as compression and noise, while achieving a favorable robustness-quality trade-off compared with prior methods. In addition, decoupled finetuning provides per-Gaussian attributions that reveal where the message is carried and why those carriers are selected, enabling auditable explainability. Compared with state-of-the-art methods, our approach achieves a PSNR improvement of +0.83 dB and a bit-accuracy gain of +1.24%.
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。
