鲁棒性与安全 · CVPR2026 ·2026.06.03

何处、何物、为何:迈向可解释的 3D-GS 水印

Where, What, Why: Toward Explainable 3D-GS Watermarking

Mingshu Cai、Jiajun Li、Osamu Yoshie、Yuya Ieiri、Yixuan Li

与你有关的部分

把水印载体选择与视觉补偿器解耦,并用通道级分组掩码限制梯度传播,这套「载体/补偿器分离 + 梯度门控」思路可直接迁移到 NeRF、高斯泼溅等隐式表示的内容溯源与版权保护,同时其逐高斯归因为隐式表示的可解释性分析提供了可复用工具。

原文依据 we introduce a channel-wise group mask that controls gradient propagation for carriers and compensators
可迁移到 3D高斯泼溅水印版权保护与内容溯源隐式表示可解释性分析

摘要译文

由模型翻译 · 术语统一 · 请对照原文核对关键结论

随着 3D 高斯泼溅成为交互式三维资产事实上的表示方式,鲁棒且不可感知的水印技术变得至关重要。我们提出一个表示原生框架,将「在何处写入」与「如何保持质量」分离。Trio-Experts 模块直接作用于高斯基元,为载波选择导出先验;而安全与预算感知门控(SBAG)将高斯分配给水印载波——在扰动与码率预算下针对比特韧性进行优化——以及分配给不受水印损失影响的视觉补偿器。为保持保真度,我们引入通道级分组掩码,控制载波与补偿器的梯度传播,从而限制高斯参数更新、修复局部伪影,并在不增加运行时开销的前提下保留高频细节。我们的设计带来视角一致的水印持久性,并对压缩、噪声等常见图像失真具有强鲁棒性,同时相比先前方法取得更优的鲁棒性—质量权衡。此外,解耦微调提供逐高斯归因,揭示消息承载于何处以及为何选择这些载波,从而实现可审计的可解释性。与最优方法相比,我们的方法取得 +0.83 dB 的峰值信噪比提升与 +1.24% 的比特准确率增益。

英文原文摘要 1376 字符

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, the 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%.

标签

会议 CVPR2026
页码 20701-20710
Bibkey
来源 CVF Open Access