MantleMark: Migrating Watermarks From Multi-View Images to Radiance Fields via Frequency Modulation
Bibliographic record
Abstract
Multi-view images are essential for modern radiance field reconstruction methods like Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS). While image watermarking is a crucial data protection and ownership verification technique, it faces unprecedented challenges in multi-view scenarios. Traditional 2D watermarking techniques often fail to maintain detectability in rendered views, while existing 3D watermarking methods are typically limited to specific reconstruction methods and require access to the reconstruction process. To address these limitations, we propose MantleMark, a watermarking framework that migrates watermarks from multi-view images to radiance fields via frequency modulation. Our key insight is constructing a mantle-like Frequency-domain Watermarking Representation in 3D frequency space, which can be projected to create view-dependent watermarking patterns. Relying upon the Fourier Projection-Slice Theorem, we embed these patterns through magnitude spectrum modulation in the image frequency domain, enabling watermarks to migrate into 3D representations. This approach ensures watermark detectability in rendered views regardless of the reconstruction methods used by adversaries. Extensive experiments demonstrate that our method achieves robust watermark detection while maintaining high visual quality across various radiance field-based reconstruction methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".