构建3D伪造检测基准，揭示2D方法失效并提出新检测思路
Fake3DGS: A Benchmark for 3D Manipulation Detection in Neural Rendering
- 基于3D高斯溅射构建可控篡改数据集
- 2D检测器在3D篡改图像上准确率不足50%
- 利用多视角一致性与高斯表示特征提升检测能力
近期3D重建与神经渲染技术（尤其是3D高斯溅射）的发展，使得对3D场景的编辑和高质量重渲染变得可行且简便，由此引发3D内容真实性的安全担忧。然而，现有研究大多局限于2D空间，3D伪造检测仍属空白。为此，本文正式定义3D伪造检测任务，提出Fake3DGS数据集，包含3D高斯溅射场景及其对应的渲染视图，其中伪造图像通过控制几何、外观和空间布局进行篡改，同时保持高视觉真实性。实验表明，当前主流2D检测器难以区分原始与3D篡改图像。为填补此差距，本文提出一种3D感知检测方法，利用多视角一致性及高斯溅射表示提取的特征。实验显示该方法显著提升3D篡改内容识别能力，验证了新数据集的有效性及超越2D证据的必要性。代码与数据已公开。
原文摘要 · Abstract (English)
Recent advances in 3D reconstruction and neural rendering,particularly 3D Gaussian Splatting, make it feasible and simple to edit 3D scenes and re-render them as highly realistic images. Therefore, security concerns arise regarding the authenticity of 3D content. Despite this threat, 3D fake detection remains largely unexplored in the literature, and most existing work is limited to 2D space. Therefore, in this paper, we formalize the concept of 3D fake detection and introduce Fake3DGS, a dataset of 3D Gaussian splatting scenes and corresponding rendered views, where fake images are produced by controlled manipulations of geometry, appearance, and spatial layout, while preserving high visual realism. Using this benchmark, we demonstrate that current state-of-the-art 2D detectors struggle to distinguish between original and 3D manipulated images. To bridge this gap, we introduce a 3D-aware detection method that leverages multi-view coherence and features derived from the Gaussian splatting representation. Experimental results demonstrate a substantial improvement in recognizing modified 3D content, underscoring the validity of the new dataset and the necessity for authenticity assessment techniques that extend beyond 2D evidence. Code and data are publicly released for future investigations.
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