Chirp-Based Image Watermarking as Error-Control Coding
Bibliographic record
Abstract
In this paper, we use post processing methods to compensate the bit errors occurred in watermark embedding and extracting. Forward error correction (FEC)-based and chirp-based techniques are applied to encode and shape the embedded watermark message so that even at the presence of some bit error rates (BERs) in the extracted watermark, the watermarking algorithm be able to successfully estimate the correct embedded watermark message. Repetition and Bose-Chaudhuri-Hocquenghem (BCH) codings are used as two well-known FEC schemes, and discrete polynomial transform (DPPT) and Hough-Radon transform (HRT) are utilized as two chirp detectors in chirp-based watermarking. Robustness of all the proposed post processing methods are tested for checkmark benchmark attacks, and we found that the chirp-based watermarking using the DPPT chirp detector offers the highest watermark extraction rate, and the best bit error compensation even at BERs of higher than 17%.
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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 | 0.001 | 0.005 |
| Open science | 0.001 | 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".