Fake3DGS: A Benchmark for 3D Manipulation Detection in Neural Rendering

arXiv cs.CV / 5/1/2026

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Key Points

The paper introduces Fake3DGS, a new benchmark dataset for detecting 3D “fake” images generated from 3D Gaussian Splatting scenes under controlled manipulations of geometry, appearance, and spatial layout.
It argues that recent neural rendering advances make realistic 3D scene editing and re-rendering easier, raising security and authenticity concerns for 3D content.
Experiments show that existing state-of-the-art 2D detectors have difficulty distinguishing original images from 3D-manipulated ones.
To address this, the authors propose a 3D-aware detection approach that uses multi-view coherence and features derived from the Gaussian splatting representation.
The dataset and code are released publicly to support further research on authenticity assessment beyond purely 2D evidence.

Abstract

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.