Bibliographic record
Abstract
In the recent years, many papers have been published on the use of singular value decomposition (SVD) for watermarking because of its robust nature. The singular values that are produced are very stable and vary very little under attacks. This introduces an ideal medium for which a watermark is embedded for robust watermarking. Nevertheless a severe flaw has been discovered by Zhang and Li in (X. Zhang et al., 2005) which is based on an image watermarking technique proposed by Liu and Tan in (R. Liu et al., 2002). In this paper the discovered flaw is explored and tested for a SVD-based audio watermarking technique proposed by Ozer et al. in (H. Zer et al., 2005) Ozer proposed a technique in which the audio signal is first transformed into matrix form using the short-time Fourier transform (STFT). The SVD is then used to decompose the STFT matrix in order to produce its singular values. As the watermark is embedded, two matrices are formed as a byproduct of the watermark and sent to the detector along with the watermarked signal. The flaw arises in the detection stage of this technique. Experiments show that the detection stage depends primarily on the passed information and depends very little on the watermarked signal. Therefore by altering the watermarked signal with various attacks gives a false sense of robustness and can easily be seen as an extremely robust system
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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 source (direct Gemma or distilled Codex), 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".