Bibliographic record
Abstract
Perceptual embedding is a technique to embed extra information into multimedia signals without fidelity degradation, which is the core of many applications including watermarking and data hiding. Perceptual embedding can be viewed as a telecommunication to transmit the embedded information over the medium consisting of the host signal. This observation divides the current embedding techniques into two categories, i.e. the host-suppressing ones like the quantization-based Quantization Index Modulation (QIM) and Scalar Costa Scheme (SCS), and the non host-suppressing ones like the conventional Spread Spectrum (SS) technique. The former class has significant advantages over the latter in robustness and data rate due to significantly reduced noise levels. In this research, the conventional SS embedding technique is modified such that it can suppress the host impact mostly. Both the theoretical analysis and simulations show that the modification significantly improve the performance of the conventional scheme and further, outperform the QIM and SCS under the case of watermarking where the attacks can be expected to be very strong. To further increase the robustness and embedding rate, measures like frequency masking effects of the Human Masking Auditory system and Forward Error Correction schemes are employed, such as Turbo code. The second part of this research explores the possibility of high-capacity embedding in telephony speech signals. Another modification to improve the embedding rate is proposed for the conventional SS scheme under weak attacks, which are expected for the case of data embedding.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".