An NGram-Based Copyright Protection for Digital Images
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper introduces an NGram-based approach for digital images copyright protection. The advantage of the proposed approach compared to the existing works, is that it does not always require the whole elements (e.g., bits) of the watermark pattern to be embedded into the original digital image. This, in turn, allows us to protect the digital images while at the same time minimizing the chances of having low quality marked digital images. The best case occurs when no element of the pattern is embedded into the original digital image. In contrast, the worst case, which rarely happens, occurs when all elements are embedded into the digital image. Moreover, the use of an NGram approach allows us to efficiently and easily reach any part of the image using the corresponding level numbers and addresses. This makes it more efficient especially for complex and high-dimensional data (e.g., images and videos). Experimental results show the effectiveness of the proposed approach in terms of the ability to recover the watermark pattern from the marked digital image even if major changes are applied to the original digital image.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.005 | 0.001 |
| 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 it