A Locatable Zero Watermarking Scheme and Visualization for 3D Mesh Models
Bibliographic record
Abstract
Nowadays, most 3D mesh model protections are based on embedding watermarking system. These methods have to change the original mesh data to achieve the watermarking. Considering the sensitivity and complexity of 3D mesh data, this paper proposes a disturbing free 'zero-watermarking' algorithm, which is based on octree partition. For each octant, we employ parameterization and singular value decomposition (SVD) to analyze the 3D geometry signal and construct the zero-watermark with corresponding octree-codec. The integrity of protected 3D mesh can be checked through computing the similarity of watermark between original and processed mesh. Moreover, the results are represented in a visual way, which means the tendency of deviation and changed region will be colored according to our merits of similarity. The experiment results verify that the algorithm is robust to various attacks including affine transformation, vetex reordering, noise addition, cropping, simplifying, and even mixed attack.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".