Bibliographic record
Abstract
This paper proposed a new digital watermarking algorithm for audio authentication to protect the audio integrity in the perceptual sense.Firstly,it framed an original digital audio and then divided each frame into two parts.Secondly,with the spatial watermarking technique,embedded synchronization code into the first part.And then performed the DCT and calculated the AFRV(adjacent-frame relationship vector) used as the watermark information on the second part.Finally embedded the watermark bit into the DCT intermediate frequency coefficients of other frame's second part.When authenticated,the algorithm could determine whether the audio had been malicious tampering by computing AFRE(adjacent-frame relationship error) and according to the pre-set threshold without the help from the origin watermark.If the data were subjected to tampering,it could find out the data error localization.Simulation results show that the proposed algorithm can not only realize the audio media integrity authentication,but also accurately identify the tamper location.
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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 | 0.002 |
| 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".