A SEMI-FRAGILE AUDIO WATERMARKING SCHEME BASED ON DIGITAL WAVELET TRANSFORM AND QUANTIZATION AND ITS APPLICATION IN POWER SYSTEM
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
We anticipate extensive applications of digital watermarking in future electric power systems, including copyright protection, content authentication, quality measurement, database indexing and retrieval. We present a semi-fragile audio watermarking scheme that embeds watermark in the discrete wavelet transform (DWT) domain of an audio by quantizing the selected coefficients. Unlike Fourier transform, the wavelet transform contains both frequency and time information. Different quantization scale can give different robustness to watermark. A matching filter is employed to locate the start point of watermark in the watermarked audio having undergone attacks. The main contributions of this paper include using watermarking for audio authentication, applying matching filter for locating the watermark start point, and proposing a practical audio watermarking scheme that can be used for both copyright protection and authentication. Experimental results demonstrate the feasibility of the scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it